Market Liquidity Risk: Implications for Asset Pricing, Risk Management, and Financial Regulation 1137390441, 9781137390448

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Market Liquidity Risk

Market Liquidity Risk Implications for Asset Pricing, Risk Management, and Financial Regulation

Andria van der Merwe

MARKET LIQUIDITY RISK

Copyright © Andria van der Merwe, 2015. All rights reserved. First published in 2015 by PALGRAVE MACMILLAN® in the United States—a division of St. Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010. Where this book is distributed in the UK, Europe and the rest of the world, this is by Palgrave Macmillan, a division of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries. ISBN: 978–1–137–39044–8 Library of Congress Cataloging-in-Publication Data Van der Merwe, Andria. Market liquidity risk : implications for asset pricing, risk management and financial regulation / Andria van der Merwe. pages cm Includes bibliographical references and index. ISBN 978–1–137–39044–8 (hardcover : alk. paper) 1. Liquidity (Economics) 2. Finance. I. Title. HG178.V36 2015 332.0415—dc23

2014048241

A catalogue record of the book is available from the British Library. Design by Newgen Knowledge Works (P) Ltd., Chennai, India. First edition: June 2015 10 9 8 7 6 5 4 3 2 1 Printed in the United States of America.

his book is dedicated to my parents for their love and support.

Contents List of Figures

ix

List of Tables

xi

Preface 1 Musings on Liquidity

xiii 1

2 Financial Crises and Liquidity Traic Jams

19

3 Market Structures and Institutional Arrangements of Trading

39

4 Asset Pricing and Market Liquidity

75

5 Stories of Liquidity and Credit

115

6 Financial Regulation and Liquidity Risk Management

143

Notes

163

Index

187

Figures 3.1 3.2 4.1 4.2 4.3 4.4

Volume versus cost per trade by security class he corporate bond credit default swap basis for high-yield and investment grade bonds he relationship between a stock’s excess return and the bid-ask spread he estimated mid, bid, and ask prices as a function of inding a market maker Mispricing and allocation of arbitrage capital he liquidity level premium and the liquidity risk premium

49 69 79 90 99 107

Tables 3.1 4.1 4.2 5.1 5.2 6.1 6.2 6.3

US ixed-income market size and trading ratio Investor types in the search-and-bargaining model Liquidity premium with and without a funding crisis Change in the credit spread of a speculative-grade irm in response to diferent size shocks in market liquidity Change in the credit spread of an investment-grade irm in response to diferent size shocks in market liquidity Available stable funding factors for net stable funding ratio Required stable factors for net stable funding ratio Example of a bank balance sheet

71 88 100 140 140 158 159 160

Preface During a recent visit to Paris, on my way to an important dinner appointment, I was stuck in rush-hour traic on the Champs Elysees. Despite eforts of looking for a way out of the misery, I was stuck, frustrated and powerless. . . . Traders and other inancial market participant may experience similar feelings of frustration when attempting to trade in an illiquid market. he low of trade in an illiquid market is hindered. Trade in such markets are characteristically slow and costly or in the extreme case of absent counterparties, impossible. Market liquidity is an assumed characteristic of a well-functioning inancial system and like traic congestions we usually start paying attention to it when broken. What exactly is market liquidity? Finance professionals typically deine market liquidity as the bid-ask spread and upon closer examination raise questions about the need for a book about the subject. he bid-ask spread captures one dimension of market liquidity. As I argue in this book the implications of market liquidityon asset pricing, risk management and regulatory policies reach far beyond this one-dimensional measure of trading cost. A rethinking of market liquidity is needed. My motivation for writing this book is threefold. First, the global inancial crisis of 2007–2008 revealed laws in our understanding of modern inancial markets in general and market liquidity in particular. Much has been written about the evolution of the crisis, explaining what went wrong. his book view the crisis as a stepping stone for developing a better understanding of the interconnectedness of markets and the common thread of market liquidity that binds particularly during times of distress. We are entering a new era of liquidity regulation with the proposed changes under Basel IIII andthe implementation of the Dodd Frank Wall Street Reform and Consumer Protection Act in the United States and the European Market Infrastructure Regulation in the Europe. he

xiv

Preface

full implications of these changes are still being monitored but theeffectscan already be seen in reduced liquidity in the corporate bond markets and unusual price dynamics in the U.S. Treasury bond market. he concepts I discuss in the book will enable you to understand the genesis of these new rules and form your own opinion about their necessity. he third motivator is the structural change brought about technological advancement—technology is changinghow and how fast market participants communicate with each other. Strategies such as high frequency trading are becoming part of the main stream vocabulary and it needs to be understood in the context of market liquidity. his book will provide you with the asset pricing and risk management tools that are essential to navigatethenew market liquidityparadigm. Tremendous gratitude goes to Chris Culp, from whom I took my irst inance class as an MBA student at the University of Chicago, Booth School of Business and who has been a mentor and a friend for many years. Chris encouraged me to write this book, without that I would not have pursued this arduous task. I also want to thank Sanjay Bhasin who opened my eyes to the intricacies of real world trading. his book would also not be possible without the input from my colleagues at Compass Lexecon, particularly Rajiv Gokhale, David Ross, Neal Lenhof, Jonathan Arnold, Jerry Lumer and Mike Keable who were instrumental in shaping my thinking about his important subject.

1

Musings on Liquidity

Liquidity is important and necessary. Liquidity is an assumed characteristic of a well-functioning market, but we only pay attention to it when it is absent—like the proverbial umbrella that is missing when it rains.1 What is market liquidity? Can we measure market liquidity? Can we manage market liquidity? his question seems trivial (or even uninteresting) to the average person who equates liquidity with money in his or her pocket. An economist oten associates liquidity with the availability of money or more speciically with the actions of the central bank, succinctly deined by the former chairman of the Federal Reserve, Ben Bernanke, in a 2008 speech on liquidity provision by the central bank: “Consistent with its role as the nation’s central bank, the Federal Reserve has responded not only with an easing of monetary policy but also with a number of steps aimed at reducing funding pressures for depository institutions and primary securities dealers and at improving overall market liquidity and market functioning.”2 Traders on the other hand equate liquidity with their ability to buy and sell securities in inancial markets. However, none of these broadly deined, supericial understandings of market liquidity were suicient to prevent the devastating and costly efects of illiquid capital markets we experienced during the 2007–2008 global inancial crisis. Subsequent events force us to develop a deeper understanding of market liquidity and beg the question whether the market liquidity paradigm has shited. Consider, for example, some ad hoc observations on liquidity that were made since the global inancial crisis. Regarding the “lash crash” of May 6, 2010, the Economist reported, “his ‘hot potato’ trading generated lots of volume but little net buying.

2

Market Liquidity Risk

Traditional buyers were unable or unwilling to step in, and the depth of the buying market for e-minis and S&P 500-tracking exchange-traded funds fell to a mere 1% of its level that morning.”3 Mark Carney, the governor of the Bank of England, observed that in 2014, “he time to liquidate a given position is now seven times as long as in 2008, relecting much smaller trade sizes in ixed income markets.”4 Carney also commented on the bizarre price swings in the US government bond market on October 15, 2014: “Fundamentally, liquidity has become more scarce in secondary ixed income markets.” hese comments were made in 2014, almost six years ater the inancial crisis! his chapter provides a historical perspective on what shapes our thinking about liquidity and highlights the premises of the neoclassical inancial ediice we need to redeine to accommodate a new market liquidity paradigm. Historical perspective on trading, liquidity, and financial markets The role of money and coins in antiquity What constitutes liquidity has evolved and mutated over time. A common theme throughout is that higher liquidity goes hand in hand with improved quality of information about the value of goods exchanged. Barter, the earliest form of exchange, was slow and uncertain partly because it provided no pricing transparency—if I trade a sword for a goat today, I may have to trade ten pigeons for the goat tomorrow and two swords for the ten pigeons next week. he value of bartered goods was purely driven by whether the seller was ofering what the buyer needed. he notion of price or value was not well deined in bartered goods, and the process of exchange was slow. Around 2000 BCE, silver and gold coins started to replace barter, giving birth to the notion that liquidity is associated with money (or money supply). Trade for coins ofered a common calibration, since the swordmaker who accepted ive drachmas5 last month would probably accept about the same amount again this month. Around the late seventh century BCE, the Lydians created metal coins embossed with royal symbols

Musings on Liquidity

3

that guaranteed the weight and purity of the coin. Standardization made coins more useful for daily commercial transactions, which led to coins being widely accepted mediums of exchange. Everyone valued the coins equally; they could readily pay any cost. While barter was slow and costly due to the process involved in examining the attributes of goods typically used in barter exchanges, coins signiicantly lowered the information cost of transacting by their uniformity, which was the issuer’s guarantee of purity. Over time, coins became emblems of great civic pride. he creation of coins and the management of their availability by ancient governments was one catalyst for the development of lively, centralized markets, such as the ancient agora, near the Athenian Acropolis, where people could meet.6 he ample supply of money lubricated trade and markets throughout the ancient world. For example, in the early years of the principate,7 the total Roman coinage per capita came to approximately 80 percent of the current US money supply.8 Ancient ports were awash with coins minted in diferent places. he irst bankers were moneychangers—they worked behind tables—tapeza in Greek, and banc in medieval Italian.9 Bankers also connected businessmen who needed funds to investors with excess funds. Since few Athenians could fully inance a trading voyage or the construction of a ship, bankers lourished and fulilled the role of the irst inancial intermediaries who brokered transactions between several parties. Bankers knew who had the money and who needed it, and they were excellent managers of risk because of their intimacy with the market. Land was a typical form of collateral for a loan. William Shakespeare’s character Shylock in he Merchant of Venice exempliied the debauchery of early bankers whose collateral demand was no less than a pound of human lesh. Migration away from money to financial market-based substitutes for money Once metal-based money was introduced, and later in the era of the gold standard, liquidity was associated primarily with money. he fact that money is the most liquid asset and, that the cost of money to pay

4

Market Liquidity Risk

for things is minimal (oten zero) is conceptually attractive. But equating liquidity only with money is too narrow. In 1936, the British Depressionera economist and father of Keynesian economics John Maynard Keynes argued that investors will move from money to assets that are risky and less liquid only if they can expect to earn a reward from them, thereby extending the notion of liquidity away from money to the quality of asset. he market and inancial economists ater the 1929 stock market crash, consumed by rebuilding the economy, were not ready for liquidity to be managed as a separate risk. Incidentally, the main application of Keynes’s liquidity preference was monetary policy.10 In 1971, US president Richard Nixon suspended the dollar’s convertibility into gold to avert the mounting crisis of a large trade deicit and a costly war in Vietnam, ending the Bretton Woods system of ixed exchange rates. Floating currencies meant that capital could low freely between countries, and it created the Milton Friedman world of free market economics. Liquidity was still thought of as money, but with the growth in Wall Street and global markets, at least in concept, liquidity gravitated toward inancial markets. Similar to earlier periods in which coins replaced barter and land was used as collateral for loans, innovation and inancial development pushed back the liquidity frontier. he Peruvian economist Hernando De Soto, who revolutionized our understanding of transforming poverty into wealth, argued more generally that the transformation of “dead capital” into “live capital” is a key step in the development process.11 Money is desirable because it acts as “stores of value.”12 In other words, money serves as an efective cushion against pressing needs during periods of potential future liquidity shortages, thereby mitigating the costs of dealing with uncertainty. Sir John Richard Hicks, one of the most inluential economists of the twentieth century and winner of the 1972 Nobel Memorial Prize in Economic Sciences, argued that other assets also have the capacity to act as stores of value.13 Hicks suggested that liquidity preference, in the narrow sense of the demand for money, could be created in inancial markets. his is exactly what the subsequent processes of inancial development throughout the post–Bretton Woods period did—they created substitutes for money. Bank-based systems have naturally produced

Musings on Liquidity

5

such substitutes by ofering deposit contracts and credit lines, which provide an option to withdraw when liquidity is needed. Competition in the inancial sector has spurred the growth of nonbank institutions that ofer new products adapted to the liquidity preference of investors. An example of such innovation is the development of index funds during the 1970s. he irst index fund available to individual investors, First Investment Trust, was sponsored by he Vanguard Group in 1976.14 Today, thousands of such funds are available to investors in the form of no-load index mutual funds and exchange-traded funds (ETFs). he modern form of mortgages15 is another example of inancial innovation that allows consumers to create stores of value, in the form of equity in their homes, when they borrow instead of inance their homes. heir commitment to reimburse interest and principal on their mortgages represents a claim on their future income. his claim can be securitized and transformed into a store of value through the institution of mortgagebacked securities (MBS). he real estate mortgages of US households have grown from 15 percent of their net wealth in 1949 to 41 percent in 2001 due to various factors, such as inancial innovation, increased risk taking through high loan-to-value ratios, teaser rates and lack of reinancing penalties, and changes in legislation favoring homeownership. he process of securitization is another example of how innovation creates a new market-based substitute for money. Securitization enables economic agents to obtain cash more readily against an array of future expected cash lows: from basic assets (loans, securities, and receivables) to other securitized products, such as subprime residential mortgage-backed securities, collateralized debt obligations (CDOs), or asset-backed commercial paper (ABCP). hese developments provided market participants with more lexibility to allocate cash lows and manage risk associated with uncertainty, but they should have raised questions about the robustness of the market-based liquidity regime, as we will discuss in more detail in chapter 2. The relevance of financial institutions and market structures In a world of ideal, frictionless markets, every commodity would be readily available such that the aggregate supply equals the aggregate

6

Market Liquidity Risk

demand. he general equilibrium economy suggested by American economist Kenneth Arrow and French economist Gerard Debreu formalized the conditions needed for the aggregate supply to equal the aggregate demand. he so-called Arrow-Debreu model is central to the theory of general equilibrium that paved the way for the development of much of classical asset pricing theory.16 In this economy, savers can hold assets, such as equity, bonds, or demand deposits, which they can sell quickly and easily if they seek access to their savings. Firms also have permanent access to the capital markets to satisfy funding needs. he arguments supporting the general equilibrium economy do not provide any insight into the important roles of inancial institutions, intermediaries, and banks in the provision of market liquidity. Trading in markets is intertemporal and involves uncertain future value. Let me illustrate this point further. Why does it seem more rational for you to buy shares in Apple than shares in the Curl Up & Dye Salon in New Mexico? he simple answer is that you have never even heard of the Curl Up & Dye Salon in New Mexico. Assumptions of frictionless markets assume that all information is readily available and that investors agree on its implications, but they do not give much insight into the process by which information would be acquired and disseminated. Gathering information is costly. In the real world, you may have to make a trip to New Mexico to learn more about the salon. If every investor pays this “ixed cost,” it would not be a very eicient use of economic resources. his creates an incentive for groups to form inancial intermediaries to economize on the cost of acquiring and processing information.17 By enforcing contracts and exchanging goods and inancial services, markets and institutions ameliorate the problems created by real-world market frictions such as the cost of acquiring information.18 Financial markets develop precisely to overcome impediments to trade. he growth in the size and the development of complex market structures leads to less direct participation by individuals in inancial markets toward more indirect participation of individuals through various kinds of agents acting as inancial intermediaries. Although their array of products and services is more sophisticated than in antiquity, banks continue to act as inancial intermediaries. Banks provide liquidity by acting as risk-sharing agents to insure against

Musings on Liquidity

7

depositors’ random consumption needs. Gorton (1990) explained the rationale for the existence of banks and deposit insurance as providing a riskless transaction medium to eliminate the need for uninformed agents to trade in assets whose returns are known by better informed agents. By issuing deposits, banks create “riskless” securities for trading purposes. In instances in which the bank asset risk is such that uninsured deposits cannot be made riskless, deposit insurance can replicate the allocation achieved with riskless private bank deposits.19 In the sixties, the economist Harold Demsetz laid the foundation for the economics of transacting. Demsetz developed arguments supporting the role of market makers or inancial intermediaries: they exist to satisfy the need for immediacy in delegated trading.20 he intermediary would be on standby, ready to transact whenever an order is placed, but he would also require compensation in the form of transaction costs for providing immediacy to trade. Demsetz proposed a simple one-period framework for thinking about market making, but actual trading mechanisms are far more complex. Market structures and institutional arrangements develop partly to accommodate this need for immediacy. How well markets achieve this goal depends on numerous factors, including the number of traders, the fragmentation of the market, the volume of trading, the regulatory environment, access to capital, and many more, which we will touch upon in later chapters. hese arguments underscore the importance of inancial market structures and acknowledge their role in price discovery. As Nobel laureate Robert Merton remarked in his seminal paper on capital market equilibrium, “To abstract from these [institutional] factors is to neglect an orderone inluence on the short-run behavior of security prices.”21 Much of the ignorance of market structure in standard asset pricing theory can be traced back to the Arrow-Debreu general equilibrium model, which argues that the equilibrium price simply equates supply and demand. he work of French economist Leon Walras shed more light on price formation in the general equilibrium economy. According to Walras, security prices are formed through a process of trial and error in an exchange of assets between buyers and sellers at no cost and without need for immediacy. he novelty of Walras’s process is the presence of a ictitious Walrasian

8

Market Liquidity Risk

auctioneer who is responsible for matching supply and demand through a process of preliminary auctions that are executed until demand perfectly equals supply, at which time the equilibrium price is achieved. he auctioneer lowers the prices for goods in excess supply and raises the prices of goods in excess demand until the markets clear. Traders have a chance to revise their orders, and no trade occurs outside this auction. he eventual market-clearing price reached by the Walrasian auction is referred to as the general equilibrium price or fundamental value. he Walrasian auction exhibits the characteristics of a theoretically perfect market: no trading frictions or trading costs, perfect competition, and symmetric information equally shared by all participants. Immediacy is also not an issue in the Walrasian market, but we know that prices can contain a cost of immediacy of trade. he Walrasian auctioneer provides an intuitively appealing answer to the price formation question. In the real markets, a complex web of traders, dealers, and market makers facilitates the process of price discovery similar to the auctioneer. But the Walrasian auction still leaves holes in our understanding, because the general equilibrium auction has no time dimension and no volume and capital constraints, which are relevant to price discovery in actual, real-world markets. What does market liquidity have to do with it? An essential ingredient for the functioning of inancial markets is liquidity. Consider the following hypothetical example of an extremely illiquid market. An eccentric billionaire decides to sell his special model, purple convertible, but only on a Tuesday. he other eccentric billionaire wants to buy this exact model, but will only do so on a Wednesday. Now, unless you can convince these two billionaires to meet on the same day, no transaction will occur. his example illustrates the intertemporal nature of market liquidity. he two billionaires may also agree on the fundamental value of the vehicle, but in this illiquid market, the transaction price is not well deined. We deine a liquid market as a market in which a large volume of trades can be immediately executed with minimum efect on price. In other words, the liquidity of the market can be recognized by how low

Musings on Liquidity

9

the uncertainties of the execution prices are. In addition, we consider “market depth,” which absorbs the price changes accompanied by trade execution, as an important factor in explaining market liquidity. In determining “market depth,” we need to take into account the size take into account the size of the potential trade. his deinition of market liquidity culminates decades of economic and inancial thought. In 1936, Keynes used terminology such as the “future volatility of market prices,” while the Nobel laureate Sir John Richard Hicks used the phrase “possibility of immediate execution of a transaction” to describe market liquidity. he author of several award-winning papers, Walter Bagehot in his 1971 paper “he Only Game in Town,”22 focused on factors such as the existence of adverse selection efects due to information asymmetry, the price impact of a trade, and the portion of trading cost that is set according to the pricing policy of the market maker. According to market microstructure, the branch of inancial economics that investigates trading and the organization of inancial markets at very short horizons, it is oten the case that more practical concepts are introduced, such as the cost of changing positions (tightness), the trade size or thickness of the order book-proile (order book refers to a data set which provides traders with bid-ask prices and volume ofered per price) required for changing prices (market depth), and the required period of time to recover from price luctuation caused by a sudden shock or to reach a new equilibrium (market resiliency).23 Nobel laureate Merton Miller and his coauthor, Sanford Grossman, pointed out that market liquidity can be measured by looking at “the ability of executing trades under the current price quotes price- and time-wise.”24 Fisher Black, one of the authors of the famous Black-Scholes option pricing model, deined the liquid market on which much of inance theory rests. he following excerpt from Fisher Black’s seminal 1971 paper, “Towards a Fully Automated Exchange,” captures the multifaceted nature of market liquidity:25 he market for a stock is liquid if the following conditions hold: (1) here are always bid and asked prices for the investor who wants to buy or sell small amounts of stock immediately. (2) he diference

10

Market Liquidity Risk

between the bid and asked prices (the spread) is always small. (3) An investor who is buying or selling a large amount of stock, in the absence of special information, can expect to do so over a long period of time at a price not very diferent, on average, from the current market price. (4) An investor can buy or sell a large block of stock immediately, but at a premium or discount that depends on the size of the block. he larger the block, the larger the premium or discount. In other words, a liquid market is a continuous market, in the sense that almost any amount of stock can be bought or sold immediately, and an eicient market, in the sense that small amounts of stock can always be bought and sold very near the current market price, and in the sense that large amounts can be bought or sold over long periods of time at prices that, on average, are very near the current market price. The theoretical argument of no arbitrage A necessary condition of equilibrium in inancial markets is the principle of no arbitrage, which is deined as “the simultaneous purchase and sale of the same, or essentially similar, securities in two diferent markets for advantageously diferent prices.”26 According to this principle, you should be able to buy the cheaper gas at the suburban gas station and sell it at a higher price at the expensive gas station downtown to make a sure proit. he reason this is not a common form of employment is because such a transaction would entail risks and would require some capital investment, both of which are assumed to be irrelevant in theoretical arbitrage. Understanding the limitations of theoretical arbitrage arguments is important. Under the no-arbitrage principle, prices should equal their fundamental (equilibrium) values. An important corollary to the no-arbitrage hypothesis is the law of one price, which says that assets with similar payofs should trade at similar prices. he other twin of the no-arbitrage theory is the eicient market hypothesis, which basically says that asset prices should change only in response to news about fundamentals; hence, asset prices follow a random walk. Arbitrage plays a critical role in the analysis of inancial markets, because it ensures that prices converge

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11

to fundamental values, essentially keeping markets eicient. But if all these theoretical arguments prove to be true, why are there still billionaire hedge fund managers? he 2013 Nobel Prize winner Eugene Fama came to the rescue with his analysis of eicient markets. Fama explained that a large number of arbitrageurs, each taking an ininitesimal position against such mispricing, transact in the inancial markets to safeguard against diferences between transaction prices of securities and their fundamental values. Consider, for example, an arbitrage strategy involving the Standard & Poor’s (S&P) 500 Index and an S&P 500 futures contract. Assume that the risk-free annual interest rate is 8 percent. Further assume that the S&P 500 Index is trading at 2,000, and that the futures contract, due to expire in three months from now, is trading at 2,039. he 2 percent diference in the price of the futures contract and the S&P 500 Index represents the time value of money or risk-free inancing for three months—there is no arbitrage opportunity given these prices. But the Federal Reserve unexpectedly decreases interest rates by 1 percent, causing the S&P 500 Index to increase to 2,060. Further assume that the price of the S&P 500 futures contract increases to 2,040. his creates an arbitrage opportunity because the price of the futures contract is below its fair value. A trader can buy the futures contract and simultaneously sell the S&P 500 Index for a proit. Similar trades by other market participants cause the price of the futures contract to increase and/or the price of the S&P 500 Index to decrease. he futures contract will then converge to its fair value, and the arbitrage opportunity will disappear. hese types of apparent arbitrage opportunities exist and may even persist for some periods of time in inancial markets. In theory arbitrageurs have unlimited access to capital and are able short securities without limitations, making them essentially risk neutral toward fundamental values. heir collective actions should drive these types of relative mispricing to zero. We argue that the theory rests on the critical assumptions of “unlimited access to capital” and “no risk.” If either of these assumptions is violated, which is oten the case in real markets and particularly in distressed markets, asset price distortions occur that are not well described by

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Market Liquidity Risk

classical asset pricing models. In chapter 4 we delve more deeply into the complexities of real-world arbitrage and the efects it has on asset pricing based on the seminal work on limits to arbitrage by Andrei Shleifer and Robert Vishny.27 Equilibrium asset pricing A central tenet/paradigm of asset pricing is that the price of an asset is the expected discounted payof.28 All asset pricing comes down to speciications of the discount factor that are useful for a speciic application. here are two branches of asset pricing in which this central idea is applied: absolute and relative asset pricing. In absolute pricing, we value a stream of cash lows based on its exposure to fundamental sources of macroeconomic risk. he discount factor captures the investor attitude toward diferent macroeconomic states. Also referred to as equilibrium asset pricing models, this class of models assumes that aggregate market risks rather than individual risks are priced based on some notion of general market equilibrium. Relative pricing determines the value of an asset given the price of some other asset. his approach uses as little information about macroeconomic conditions as possible. To ind the value of a McDonald’s hamburger, absolute pricing starts thinking about how much it costs to feed a cow. Relative pricing looks at the price of a hamburger at Burger King. In inance, option valuation and corporate inance (the use of comparable investments to determine the required rates of return) are the prime applications of relative pricing methods. he Black-Scholes formula is another example of relative pricing—it expresses the option price given the stock and bond prices. Asset pricing concepts can help investors and hedgers distinguish properly between systematic and idiosyncratic risks. Using these basic principles, we can evaluate the components of market liquidity risk that should be the focus of asset pricing. For example, why would market liquidity be impacted by irm-speciic idiosyncratic liquidity risk such as the CFO’s inability to properly manage future cash low needs of the balance sheet? he generality ofered by absolute pricing should be weighed against the simplicity of relative pricing. Most models, however, are

Musings on Liquidity

13

a blend of absolute and relative approaches. Understanding the principles of equilibrium asset pricing is important because it gives insight into the limitations of pure theoretical models to real-world inancial markets. We can think of the asset price as the value or worth to the investor. he classical pricing theory argues that investors do not value money directly, but recognizes that it is the pleasure or “utility” of the consumption that money can buy what really matters. Speciically, people value money more if it comes sooner, and if it comes in bad times when they really need it, rather than in good times when they are already doing well. he following equation formalizes these considerations mathematically. Consider an asset with a single cash low or payof at a time in the future. he price or value of this asset to the investor is  u (c )  Pt = Et β c t +1 xt +1  .  uc (ct )  he discount factor β is slightly less than one to relect the “time-value of money” or, put diferently, the investor’s preference for money sooner rather than later. he “utility function” uc decline as c, consumption, increases (a college student values $100 much more than the billionaire hedge fund manager). We can rewrite this equation to relect an investor’s optimal investment decision: Pt × uc (ct ) = Et β × uc (ct +1 ) × xt +1  . he true cost is the price of the asset (how many dollars the investor had to give up) times the value of a dollar (utility cost to the investor) uc (ct) at time t. he true beneit is the expectation Et of the dollar payof xt+1 times the value of the dollar uc (ct+1) at time (t + 1), times beta, which discounts the future value of utility back to time t. The fundamental value equation Most inancial economists do not think too much about consumption and utility functions, but ind it more intuitive to think in terms of discount

14

Market Liquidity Risk

factors. To establish a link between this thinking and the basic equation, we deine a stochastic discount factor as the rate at which an investor is willing to substitute one unit of consumption now for one unit of consumption later: mt +1 = β

uc (ct +1 ) uc (ct )

.

he discount factor is stochastic or random because of the uncertainty about future consumption and about the exact economic environment that will prevail in the future. Using this notion of a stochastic discount factor, we can recast the pricing equation in terms of an expected discounted payof, Pt = Et [mt +1 xt +1 ]. he randomness of the discount factor as an indicator of future prosperity captures the intuition that assets that pay of well during adverse conditions, such as insurance, are particularly valuable and should be priced higher than assets that do not share this property. Systematic versus idiosyncratic risk Asset pricing is about risk and reward. Identifying risks and assessing the premium one earns for bearing risks are two central questions in asset pricing, and the point of theory is to provide the necessary quantitative tools to answer these questions. Risk managers tend to classify risk as market, credit, or liquidity. Financial economists tend instead to distinguish between “systematic” and “idiosyncratic” risk. Not all risks are bad—you only earn a premium over the risk-free interest rate by taking on some risk. A central idea in asset pricing is that only systematic risk generates a premium. Idiosyncratic risks are “not priced,” meaning that you earn no more than the interest rate for holding them. It is the job of risk managers to get rid of idiosyncratic risks. To understand what “systematic” and “idiosyncratic” mean in context, we can rewrite the fundamental value equation as

Musings on Liquidity

Pt =

Et [ xt +1 ] Rf

15

+ cov (mt +1 , xt +1 ) .

his equation says that the asset price is equal to the expected cash low discounted at the risk-free rate, plus a risk premium. he premium depends on the covariance of the payof with the discount factors. his covariance is typically a negative number, so most assets have a lower price than otherwise (or a higher average compensation for risk). Recall that the discount factor is an indicator of bad times. Most assets pay of well in good times. hus, most asset returns and payofs covary negatively with the discount factor. he converse case drives home the intuition. Insurance is a terrible investment. he average return is negative—you pay more in premiums than you receive, on average, in settlements. Yet people willingly buy insurance. Why? Because insurance pays of in bad times—the value of insurance is higher than predicted by the standard present value formula, because the covariance term is positive. Financial assets are “anti-insurance,” and it is this feature, and only this feature, that generates a risk premium and allows assets to pay more than the interest rate. his equation has a dramatic implication: a risk may be very large in the sense of having a high variance, but if it is uncorrelated with the discount factor, its covariance is zero and it generates no premium. Its price is just the expected payof discounted at the risk-free rate. he volatility of the asset’s cash low per se is completely irrelevant to its risk premium. he covariance of the cash lows of the asset with consumption is much more important to how buying the asset afects consumption—what investors care about in the end—than the volatility of the asset’s cash low. Now we can really understand and precisely deine “systematic” versus “idiosyncratic” risk. he systematic part of any risk is that part that is perfectly correlated with the discount factor. It is the part that generates a risk premium. he idiosyncratic part of any risk is that part that is uncorrelated with the discount factor; it generates no premium. In the modern version of the theory, systematic means correlated with the investor’s marginal utility. his is true no matter what “asset pricing model”—no matter what speciication of the discount factor is correct.

16

Market Liquidity Risk

A critical question for practical application remains: what data do we use for the discount factor m? he search to connect the discount factor to actual data has led to many “named” asset pricing models. hese models are just special cases of the fundamental value equation. he single-factor CAPM provides a valuable conceptual framework for thinking about risk and return. he CAPM was developed by Nobel laureate William Sharpe29 and later extended by Fisher Black.30 he CAPM is one special case of the general theory. It speciies that the discount factor is linearly related to the market return. Hence it deines systematic risk for every asset by regressions of returns with the market portfolio return. In the CAPM, a constant of proportionality, beta, tells us what the relationship is between the expected return of a particular asset and the expected return on the market portfolio: E(R j ) = R f + β j  E(R m ) − R f  , where Rj and Rm denote one-period arithmetic returns on asset j and the market, Rf denotes the risk-free rate, and where βj is the regression coeficient of the return on the market, or

βj =

cov ( R j, R m ) var(R m)

.

he CAPM is mathematically identical to a speciication of the discount factor that is linear in the market return, rather than linear in the consumption growth. he CAPM discount factor model is a sensible approximation of the fact that most people are unhappy when the return on the market goes down. But it is clearly only an approximation. To derive the CAPM formally, you need to state assumptions under which a linear function of the market return is a completely suicient indicator of good and bad times. Keep in mind that the CAPM is not an alternative to the consumptionbased model. It is a special case. Now, consumption surely goes down when the market return goes down, but, in the real world, other things matter as well. In addition, all investors in a CAPM world must hold the same portfolio of assets—the market portfolio.

Musings on Liquidity

17

Once dismissed as an institutional friction that is “assumed away” in complete markets, it seems that assets paying of poorly in times of poor market liquidity must pay higher average returns. Put diferently, the discount factor is afected by liquidity. he marginal utility of a US dollar, delivered in the middle of a market meltdown, such as ater the Russian bond default and LTCM collapse, may well have been very high. Summary Money is the most liquid asset, and the cost of using money to pay for things is minimal, oten zero. If inancial assets such as stocks, bonds, and loans shared these attributes, they could be circulated as money. But they do not share these attributes, their futures prices are uncertain, and buying and selling them is costly. Each asset has its own liquidity proile that typically varies by how transparent its economic value is and how easy it is to communicate such features credibly to a large investor base. he symmetric information-based asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been completely solved. A security whose lowest returns tend to accompany unfavorable shits in its marginal utility must ofer additional compensation to investors for holding the security. Liquidity appears to be a good candidate for a priced state variable. It is viewed as an important feature of the investment environment and the macro economy. In other words, if market-wide liquidity is indeed priced, it seems reasonable that many investors might require higher expected returns on assets whose returns have higher sensitivities to aggregate liquidity. Yakov Amihud presented compelling evidence that market liquidity is priced in stock markets globally.31 Amihud’s study of equity market data from 45 countries shows that the average return on the most illiquid stocks are signiicantly higher than the average return on the most liquid stocks, ater controlling for global and regional common risk factors. he empirical results also ind strong commonality in the liquidity premium—market liquidity should therefore be incorporated as a systematic risk in asset pricing.

2

Financial Crises and Liquidity Traffic Jams

Overview of financial crises and latent risks During noncrisis times, buyers and sellers usually show up in most markets to trade, and they can all go on to do what they usually do: invest, hedge, and speculate. During such noncrisis times, regulators monitor inancial markets using established policy frameworks. However, during times of inancial distress, these seemingly normal market operations may be wholly or partly impaired, for either shorter or longer time periods impeding the activities of buyers and sellers in the market and forcing regulators to question existing policies. A liquidity crisis culminates in the market’s inability to absorb transactions without violent price adjustments that are unrelated to fundamental value. In its most simplistic form, a liquidity crisis is characterized by an extreme widening of bid-ask spreads, and in the most extreme cases, it is characterized by the total disappearance of a market and the inability to trade. John Maynard Keynes eloquently captured the nature of the liquidity of a inancial asset when he explained that an asset can only remain liquid if it is not simultaneously put to the test by all investors.1 It is precisely this paradoxical nature of liquidity that makes it so diicult to capture liquidity risks. In this chapter, we will investigate latent (masked) liquidity risks, which were exposed during the 2007–2008 inancial crisis when contagion and irrational behavior toppled the rule of liquid inancial markets. he irst latent risk arose because assets that were deemed liquid and safe turned out to be illiquid, thereby causing tremendous market dislocation. We will look at the behavior of highly rated money market mutual funds

20

Market Liquidity Risk

(MMFs) in particular. A well-known example is the Reserve Primary Fund, a safe and conservative MMF that “broke the buck” ater the failure of Lehman Brothers in September 2008. Since these investments are considered safe and liquid, most market participants, including regulators, did not consider the liquidity risk posed by these funds. he second latent risk is the market participant’s reliance on shortterm funding. he consequences of reinancing risk were particularly detrimental for collateralized short-term borrowing when the market liquidity of assets used as collateral declined during the crisis. When the irst losses on the subprime positions arrived in early 2007, investment banks, hedge funds, and many commercial banks were heavily exposed to reinancing risk in wholesale debt markets. his exposure was a key lever in generating, amplifying, and spreading the consequences of the collapse of money markets during the crisis.2 A third latent risk and a key characteristic of the 2007–2008 crisis was the failure of the interbank market to redistribute liquidity. Due to the central role of the interbank market, the disruptions and mispricing caused in part by an initial liquidity shock in relatively isolated corners of the market were transmitted across markets by inancial institutions and trades that straddle markets. Yale professor Gary Gorton observed that asymmetric information about the size and location of risk, and the accompanying fear of counterparty default, which was created by the complexity of securitization, was at the heart of the inancial crisis.3 A relatively benign reduction in real estate prices in the United States increased the probability of default in structured asset-backed securities (ABS) and their related structured credit products. his was a credit event, but as portfolios became riskier, it raised questions about the value of these securities and this uncertainty transpired as market illiquidity in these securities. he market illiquidity caused diiculty in the inancing and reinancing of these products, which led to funding illiquidity. he realization that these products span multiple markets increased the prospect of contagion. he market illiquidity in structured products preceded funding illiquidity, which was detrimental to the interbank market, causing persistent dislocations in this market during 2008.

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Markets were used as a source of liquidity. Market participants who needed to sell assets could either sell other more liquid securities or sell securitized bonds at deep discounts. When liquid bankers irst supply cash in exchange for assets, they create an imbalance in the system. hey are increasing their holding of illiquid assets and reducing their holding of liquid assets. If these large, illiquid bankers are subsequently hit by a liquidity shock, they have even more assets that need to be sold, thus producing greater ire sales and reducing asset prices further. Our focus is on the implications of the crisis for market liquidity and, in particular, identifying the potential modiications that are needed in the traditional asset pricing models. References to several excellent discussions on the causes of the credit crisis in summer 2007 and its development from the relatively small subprime market shocks into a global liquidity crisis in 2008 are provided in the Appendix to this chapter. Background—summary of securitization Securitization of inancial assets experienced a dramatic risk and fall in the decade before the recent inancial crisis. According to data from the Securities Industry and Financial Markets Association, the issuance of structured inance products grew from $25 billion–$40 billion in the beginning of 2005 to a peak of about $100 billion by the second quarter of 2007. By the irst two quarters of 2008, issuance dropped as low as $5 billion per quarter.4 Market participants failed to consider all the risks (particularly certain low-probability risks) associated with some types of securities, and therefore perceived highly rated bonds, including highly rated tranches of securitizations, to be less risky than they actually are. Credit ratings became the industry’s stamp of approval, and most participants did very little analysis beyond this one default-oriented metric. In general, there was limited understanding and mostly an absence of any measure of liquidity risks. hose who purchased the AAA-rated tranche of a collateralized debt obligation (CDO) had no reason to believe that the investment was risky. Due to the apparent safety of these securities, the demand from these participants was too high relative to the true risks in these securities. his outsized demand created incentives for inancial

22

Market Liquidity Risk

intermediaries to generate securities that appeared safe, thus increasing the supply of securities with hidden risks. During normal times, the perception of abundant liquidity is suicient for markets to function. But during stressful times, the inherent instability and the ignored illiquidity of the markets are revealed to preclude normal functioning An unexpected, oten trivial shock to these securities caused their value to fall sharply as investors dumped them upon realizing their true risks. hese dynamics can explain the malfunctioning of the collateralized debt market, particularly the run on repurchase agreements (wholesale funding). Liquidity risk was not priced, but the market’s excessive reliance on ratings was also inherently lawed. he ratings process had several deiciencies, such as the weakness of models and assumptions about correlations, poor understanding of the solvency of issuers and guarantors, conlict of interest emerging from repeated relationships between investment banks whose involvement in structured inance has become substantial and rating agencies, and bundling of services by ratings agencies. Systemic risks were exacerbated by the reliance on and use of shortterm funding, because bank lenders are less likely to roll over their shortterm funding if they observe adverse signals about the bank’s assets. When banks have assets in common, adverse signals about one bank are informative about other banks, leading to widespread funding freezes. he resulting lack of funding can cause many banks to have to liquidate assets ineiciently or even be bailed out. Latent liquidity risks Money market securities safety and liquidity: dressing like money is not equivalent to acting like money he risks of MMFs have received relatively little attention in the academic literature because of their impressive record of price stability. MMF prospectuses must warn that “it is possible to lose money by investing in the Fund,” but before 2008, investors virtually never lost anything. Investors usually lock to MMFs during periods of heightened uncertainty due to their perceived safety. US Securities and Exchange Commission (SEC) Rule 2a-7 is speciically designed to restrict the credit, interest-rate, and

Financial Crises

23

liquidity risks that MMFs can take to maintain a stable net asset value (this rule does not apply to other types of mutual funds). he net asset value of MMFs is usually maintained such that the value of a share in the MMF is exactly $1. Before 2008, in the event that losses occurred despite these restrictions, the sponsors of the fund would absorb the losses to prevent the fund’s net asset value (NAV) from declining. he role of the fund sponsor is succinctly summarized in the classical reference guide on money markets, Stigum’s Money Market: “a money fund run by an entity with deep pockets, while it may not have federal insurance, certainly has something akin to private insurance . . . [and] that insurance is likely to prove adequate to cover any losses sustained by the fund.”5 When the Reserve Primary Fund, a AAA-rated MMF, lowered its share price below $1 (in other words, it “broke the buck”) due to its exposure to defaulted Lehman debt, it forced participants, including the regulators, to introduce a new paradigm that would prevent a repetition of these events. A recurring theme in the narrative of the MMF performance during the 2007–2008 inancial crisis was the devastating efects that ignorance of liquidity risk had. he policy responses to the crisis in MMFs have recognized liquidity risk as a separate risk that needs to be explicitly managed over and above the default or credit risk. Researchers and policymakers are focusing on portfolios since many ex-ante risk proxies explain a large portion of the substantial variance in outlows during the crisis. Portfolio risk, as measured by gross yield, was a signiicant and economically important predictor of outlows during the run in 2008. It is interesting to note that studies have found that another possible indicator of portfolio risk— whether a fund had a triple-A rating—was of little use in predicting crisis outcomes.6 he discussion below closely follows the work done by the Federal Reserve Board of Governors economist Patrick McCabe. MMFs that took more portfolio risk earned higher returns than their risky counterparts during normal periods of money market liquidity, but the riskier MMFs fared relatively worse during periods of low liquidity. As observed previously, the Investment Company Act documented a substantial increase in the portfolio risk of the Reserve Primary Fund

24

Market Liquidity Risk

beginning in mid-2007, just over a year before its share price fell below $1, or “broke the buck”—a very unlikely occurrence for MMFs. In a study on the efectiveness of the Federal Reserve’s Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), economists at the Federal Reserve Bank of Boston found that MMFs with greater Asset-Backed Commercial Paper (ABCP) exposures initially sufered larger outlows during the run in 2008, but recovered quickly with the announcement of the AMLF.7 Furthermore, the SEC in 2010 adopted amendments to Rule 2a-7 that impose further constraints on portfolio risk, including new liquidity requirements for MMF assets, “to make money market funds more resilient and less likely to break the buck as a result of disruptions such as those that occurred in the fall of 2008.”8 he SEC introduced an amended Rule 2a-7 that require MMFs to maintain a suicient degree of liquidity necessary to meet reasonable foreseeable redemption requests and reduce the likelihood that a fund will have to meet redemptions by selling portfolio securities into a declining market. MMFs generally have a higher and less predictable volume of redemptions than other open-ended investment companies. heir ability to maintain a stable net asset value will depend, in part, on their ability to convert portfolio holdings to cash to pay redeeming shareholders, without having to sell assets at a loss. he liquidity of fund portfolios became a critical factor in permitting them to absorb very heavy redemption demands in the fall of 2008 when the secondary markets for many shortterm securities seized up. he SEC added three new provisions to Rule 2a-7, which address different aspects of portfolio liquidity. hese will result in MMFs that are better able to absorb large amounts of redemptions. he three areas are discussed in more detail below: the general liquidity requirement, limits on the acquisition of illiquid securities, and minimum daily and weekly liquidity requirements. General liquidity requirement

To comply with this general liquidity requirement, MMF managers should consider factors that could afect the fund’s liquidity needs, including the

Financial Crises

25

characteristics of an MMF’s investors and their likely redemptions. For example, some shareholders may have regularly recurring liquidity needs, such as to meet monthly or more frequent payroll requirements. Others may have liquidity needs that are associated with particular annual events, such as holidays or tax payment deadlines. A fund should also consider the extent to which it may require greater liquidity at certain times when investors’ liquidity needs may coincide. In addition, a volatility or more concentrated shareholder base would require a fund to maintain greater liquidity than a stable shareholder base consisting of thousands of retail investors. Limits on acquisition of illiquid securities

Illiquid assets would impair a fund’s ability to meet redemption demands. Illiquid securities are deined as securities that cannot be sold or disposed of in the ordinary course of business within seven days at approximately the value ascribed to them by the MMF—this is essentially the marketbased price.9 Illiquid securities are limited to 5 percent of total assets. Illiquid securities may be high-quality securities such as term repurchase agreements, some time deposits, and insurance company funding agreements. Minimum daily and weekly liquidity requirements

he SEC also adopted new liquidity requirements that mandate each MMF to maintain a portion of its portfolio in cash and securities that can readily be converted to cash. Daily liquid assets include cash (including demand deposits), Treasury securities, and securities (including repurchase agreements) for which an MMF has a legal right to receive cash in one business day. Weekly liquid assets include the same assets, except that the fund would have had to have the right to receive cash in ive business days rather than one. he SEC proposed to include Treasury securities regardless of their maturity in the liquidity baskets because they have been the most liquid assets during previous times of market stress. Indeed, empirical evidence shows that investor “light to liquidity” that happens during times of uncertainty makes it easy to sell Treasuries

26

Market Liquidity Risk

even in large quantities.10 he SEC also included agency notes (i.e., direct obligations of Federal government agencies and government-sponsored enterprises) as daily and or weekly liquid assets. Very short-term agency notes are likely to be suicient under stressful market conditions to treat them as weekly liquid assets. he depth of liquidity in the secondary markets for these securities was another motivating factor for their inclusion in the liquid asset basket. MMFs typically invest a signiicant portion of their assets in repurchase agreements, many of which mature the following day and provide an immediate source of liquidity. he amended Rule 2a-7 limits MMFs to investing in repurchase agreements collateralized by cash items or government securities in order to obtain special treatment of those investments under the diversiication provisions of Rule 2a-7. his change is designed to reduce the risk that a MMF would experience losses upon the sale of collateral in the event of counterparty default. Short-term debt, rollover risk, and pledgability of collateral he amount of cash holdings and the maturity structure of long-term debt are important considerations of the liquidity risk management of irms. hese choices involve a number of trade-ofs for the irm. An advantage that short-term inancing has for irms in good inancial health is that it can readjust its maturity structure more quickly in response to changes in its asset value. his follows from the traditional method of choosing the maturity structure of debt, namely matching the interest rate sensitivity of its liabilities to that of its assets in order to shield a irm against changes in interest rates.11 However, the traditional view is agnostic to the fact that a irm can be exposed to sources of risk unrelated to changes in interest rates. For example, a irm with shorter maturity debt faces increased cost due to more frequent reinancing. Factors outside a irm’s control, such as a change in market conditions, could increase the cost due to higher interest rates at reinancing. A irm could also face the risk that lenders may underestimate the continuation value of the irm and not allow reinancing to take place, leading to an ineicient liquidation of the irm, or the sale of important irm

Financial Crises

27

assets at ire-sale prices.12 One way to reduce the risk is to hold excess cash reserves, which can be expensive in practice.13 Two components of the short-term credit markets saw a signiicant growth in the decade before the crisis. he inancial market relied extensively on ABCP and repurchase agreements to fulill their short-term funding needs. Institutional cash pools played an important role in the growth of collateralized, short-term debt. Growing institutional cash pools created a demand for safe and liquid short-term debt that was met in part by securitization and other inancial innovations.14 Below we discuss the latent liquidity risks in commercial paper and repurchase agreements. Both of these are examples of cases in which short-term debt is collateralized or backed by assets. Given that one wants to use assets as collateral, the amount of debt that can be issued depends critically on the value of the underlying assets or, in the words of MIT professor Bengt Holmstrom, and 2014 Nobel laureate Jean Tirole, what matters is the “pledgability of various assets.”15 Asset-backed commercial paper

Since the mid-1980s, banks have moved an increasing volume of assets of their balance sheets and funded them through ABCP programs. hese bankruptcy remote “paper companies” issue short-term debt in the commercial paper market. Traditionally, ABCP programs inanced receivables from noninancial companies, but over time they increasingly inanced a wider range of assets, including highly rated mortgage-backed securities (MBSs) and other ABS. By the end of 2006, ABCP outstanding in the United States had grown to $1.1 trillion, larger than the amount of unsecured (nonasset-backed) commercial paper outstanding. he short-term debt of ABCP programs consisted of ABCP with an average maturity of 90 days and medium-term notes with an average maturity of just over one year. he short-term assets are called “asset backed” because they are backed by a pool of mortgages or other loans as collateral. In case of default, the owners of the ABCP have the power to seize and sell the underlying collateral assets. he shorter term of ABCP caters to the preferences of investors since it allows them to withdraw funds at short notice to accommodate their own funding needs.16 Investors might suddenly

28

Market Liquidity Risk

stop buying ABCP, preventing the vehicles from rolling over their shortterm debt. To ensure funding liquidity for the vehicle, the sponsoring bank grants a credit line, called the “liquidity backstop.” As a result, the banking system still bears the liquidity risk from holding long-term assets and making short-term loans, even though these are usually of-balancesheet entities.17 ABCP, as well as the underlying ABS trade in the over-the-counter (OTC) market in which volumes and prices of trade are opaque to anyone not directly involved in a particular transaction. Neither the investors in such securities nor the arrangers anticipate frequent trading of these securities. In fact, buy-and-hold investors are typically the most active purchasers of these securities. As it became evident that the same ABS and the structured credit products referencing those securities were likely to perform worse than anticipated, valuations became more uncertain. Mounting concerns about the default risk of subprime and other mortgages, which started in summer 2007, caused a contraction in ABCP. Outstanding ABCP shrank by $190 billion (almost 20%) in August 2007, while yields soared and maturities shortened for new issues. Outstanding ABCP fell by an additional $160 billion by the end of the year.18 As is typical in the OTC market, the liquidity of the ABCP market is diicult to measure. However, one measure of illiquidity and investor risk aversion was the degree to which the average maturity of the paper shortened from August onward. Maturities of US ABCP range from one day to approximately three months. he average maturity in May 2007 was 24 days, with some 66 percent of outstanding ABCP having maturities of less than nine days. he liquidity in the ABCP market declined as the severity of the inancial crisis increased. By August 2007, the average maturity was a mere 18 days, and 79 percent of outstanding ABCP had maturities of less than 9 days. Some normalization occurred in September, ater injections of liquidity by the Federal Reserve, but as of October 2007, the average maturity was still lower than prior to the disruption. It is also notable that the amounts outstanding of the ABCP, in which uncertainty about what backs the commercial paper is still present, have declined steadily, indicating funding liquidity using ABCP (versus nonasset-backed CP) is still impaired.

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Repurchase agreements

Another important trend in the period leading up to the crisis was investment banks’ increased reliance on short-term repurchase agreements as a form of balance sheet inancing. he growth in repurchase agreements was partly demand driven—repurchase agreements provided a vehicle whereby large institutional money pools, whose cash holdings far exceeded insured deposit limits, could lend short term to a inancial institution and receive collateral as protection. In a typical repurchase agreement, or repo, a irm borrows funds by selling an asset (collateral) today and promising to repurchase the same or a very similar asset at a later date. he lender changes a fee in the form of a margin or “haircut” to the borrower. For every $100 of collateral, an institution can receive $(100-x) in loans, with $x representing the “haircut” and (1/x) the allowable leverage. he amount of the haircut represents the amount of capital that the borrower needs to put up. If the haircut is higher, the amount of capital needed to enter into such an agreement is higher, efectively translating into a higher cost of funding. Precise estimates of the total size of the repo market are not available, but the order of magnitude is always in the trillions of dollars. he majority of this increase was due to overnight repurchase agreements, which roughly doubled from 2000 to 2007. Term repos with a maturity of up to three months have stayed roughly constant as a fraction of total assets. he greater reliance on overnight inancing required investment banks to roll over a large part of their funding on a daily basis. Any reduction in funding liquidity could therefore lead to signiicant stress for the inancial system. According to a study by Gary Gorton and Andrew Metrick, the average haircuts were near zero on most types of collateral before the onset of the crisis.19 he relatively cheap inancing enabled institutions to borrow fast amounts with little capital investment. Haircuts started increasing at the time of the subprime panic, and continued a steady rise throughout 2008. How does an increase in haircuts drive the banking system to insolvency? Consider the following illustrative example: assume the size of the repo market is $10 trillion. If haircuts are zero, then the amount of inancing that banks can achieve would equal the size of the repo market.

30

Market Liquidity Risk

When the weighted-average haircut increase and reaches, say, 20 percent, then banks essentially have a shortage of $2 trillion since they have to inance the haircut with their own capital. In the early stages of the crisis, some of this amount was raised by issuing new securities. But as the crisis worsened into 2008, this shortfall in capital increased. In their seminal study, Gorton and Metrick reported that repo haircuts increased from approximately 10 percent in January 2008 to approximately 45 percent in September 2008 following Lehman’s failure.20 Furthermore, selling the underlying collateral drove asset prices down, which reinforced the cycle: lower prices, less collateral, more concerns about solvency, and everincreasing haircuts. As the crisis progressed, investors’ fears about the value of collateral snowballed to the point that lenders were no longer willing to provide short-term inance at historical spreads and haircuts.21 For every trillion dollars in the repo market for these nongovernment assets, each 1 percent increase in haircuts is equivalent to a $10 billion withdrawal of liquidity from the system; therefore, a 25 percent rise from July 2007 to the eve of the Lehman failure represents a large drain. Following the Lehman failure, the index rose by an additional 20 percentage points, including 100 percent haircuts (which is equivalent to no trade) for some assets. he drain of market-wide liquidity led to signiicant premia on even the safest assets. The interbank market’s role in creating liquidity shortages he interbank market plays a key role in the functioning of the inancial markets. Central banks rely on interbank markets for the transmission of monetary policy. Banks rely on the interbank market to manage their liquidity by borrowing from and lending to their peers, a function that cannot be fulilled by a bank’s access to customer deposits. Smooth functioning of the interbank market is therefore critical in maintaining stability of the broader inancial market. In normal times, interbank markets are among the most liquid in the inancial sector. Problems developed in the interbank market starting in August 2007, and as the inancial crisis deepened, liquidity in the interbank market dried up by September 2008.

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31

Does the reason for the freeze in the interbank market provide any insight into market liquidity more generally? While banks are privately informed about the risk of their own assets, they cannot observe the assets and the corresponding risk exposures of their counterparties. Lending banks in the interbank market accordingly faced counterparty risk stemming from the risk of the borrowing banks’ assets. Banks may need to realize cash quickly due to the demands of customers who draw on committed lines of credit or their demandable deposits. hese banks can borrow from other banks with liquidity. If the borrowing is collateralized by risky assets, it may inhibit the bank’s ability to repay the loan in the interbank market. At the onset of the inancial crisis in 2007, many banks used complex securitized products as collateral for borrowing. he 2007 crisis was initiated with a shock to the subprime mortgage market. As explained by Gorton, the complexity of these securities made it impossible to know exactly where the risks were located, so all securitized asset classes were penalized with depressed prices and illiquidity in the interbank market, as relected in the Libor-OIS spread, a standard measure of tensions in the interbank market: Other asset classes only experience diiculties when there are problems in the interbank market, starting in August 2007. . . . he LiborOIS spread jumps in August 2007, and again when Lehman fails. Other securitized asset classes, with nothing to do with subprime, like credit card receivables, auto loans, and student loans, all move with the proxy for the state of the inter-bank market, not with the ABX. he key question for understanding the panic is: Why were non-subprime-related asset classes afected? Subprime mortgage originations in 2005 and 2006 totaled about $1.2 trillion . . . a large number to be sure, but not large enough to cause a systemic crisis. How was the shock turned into a panic? he shock was combined with asymmetric information about the locations and sizes of exposures to subprime.22 According to the European Central Bank, counterparty risk, ampliied by asymmetric information about the location of risky assets, was a key friction that caused persistent dislocations in the interbank market during the crisis.23 During times of distress, higher levels of risk cause adverse

32

Market Liquidity Risk

selection in the interbank market. Suppliers of liquidity cannot distinguish between safer and riskier banks, and do not have a way to protect themselves against banks with riskier assets. his negative externality on safer banks is so costly that they leave the market. Liquidity is still traded, but the interest rate rises to relect the presence of riskier banks. When the risk of asymmetric information is high, interbank markets may break down, as occurred during the 2008 phase of the crisis. Banks with suicient balance sheet liquidity preferred to hoard liquidity instead of lending it out, in order to avoid adverse selection. In the extreme, even riskier borrowers ind the interest rates on interbank loans too high and prefer to obtain liquidity elsewhere. Depressed lending and low prices for illiquid assets usually accompany high returns on holding liquidity.24 During the inancial crisis, we observed several points of transition in the level and distribution of counterparty risk. Around the summer of 2007, it became clear that subprime MBSs were held in bank portfolios and bank-sponsored conduits. A further revision of expected default risk occurred ater the collapse of Lehman Brothers in September 2008. his led to dramatic increases of unsecured rates and the excess reserves banks were required to hold, as well as the inability of massive liquidity injections by central banks to restore interbank activity. On November 9, 2008, the Financial Times reported, “Neither the recent massive money injections, the coordinated lowering of interest rates nor the use of public funds to recapitalize banks have done much to restart interbank lending. his action did not solve the underlying problem preventing interbank lending: extreme information asymmetry.” he freeze in the interbank market highlighted the criticality of symmetric information for market liquidity. Liquidity crisis—implications for asset pricing he latent liquidity risks had widespread consequences during the crisis, but their efect also extended beyond the 2008 period. he crisis exposed limitations in traditional inancial economic theory that cannot be ignored. We discuss the limitations of economic argument by looking

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33

more closely at no arbitrage. We conclude this section with an overview of the challenges the crisis poses for asset pricing models. Asset prices and the speed of arbitrage No arbitrage forms the basis of much of modern asset pricing. It is the principle on which most derivatives pricing, such as the Black-Scholes model, is based, and it ensures that economic relationships between fundamental securities hold. Many such relationships broke down during the crisis because many arbitrage relationships that had long been taken for granted by inancial economists stopped working. Textbook arbitrageurs operate in a frictionless economy that enables them to trade instantaneously when prices deviate from fundamental values. In contrast, the real-world convergence of price to fundamental values can be slow. In the event of capital dislocations, market participants may be capital constrained, which prohibits them from “correcting” mispricing, thereby causing persistent price deviations from fundamental value.25 Professors Mark Mitchell and Todd Pulvino looked at various popular arbitrage strategies implemented by hedge funds such as convertible bond arbitrage, whereby the payof of a convertible bond can in theory be replicated using other traded securities. During the distress in the bond market in 2005, the prices of convertible bonds decreased relative to the price of the replicating portfolios because hedge funds arbitrageurs experienced capital dislocations due to investor redemptions. During market downturns, when it could take time to raise funding (if raising capital is even possible), the prices of securities can become depressed relative to their fundamental value. Due to various institutional frictions, securities with (almost) identical cash lows can have very diferent margins. For example, consider a corporate bond and a credit default swap, both of which will provide an investor with credit exposure to a particular irm. Corporate bonds are considered “cash securities,” and typically involve an amount of upfront cash investment. To get credit exposure through a corporate bond, one must actually buy the bond for cash and try to fund it using a repurchase

34

Market Liquidity Risk

agreement that uses a broker’s balance sheet. he secondary corporate bonds markets typically have low liquidity, and selling a bond, particularly during times of market distress, can be diicult and time consuming. A credit default swap is a derivative security that is “unfunded” in the sense that it requires relatively minimal upfront capital. A small margin may be necessary only to limit counterparty risk. Under theoretical no-arbitrage arguments, the diference in the yield spread on the corporate bond and the premium on the credit default swap, referred to as the basis, will be zero. Professors Nicolae Gârleanu and Lasse Pedersen studied the negative basis that developed and persisted for a period of months between credit default swaps and corporate bonds during the crisis. he persistence of the nonzero basis constitutes a failure of the principles of no arbitrage, and as explained by Gârleanu and Pedersen, it can be traced back to the diference in margins requirements on corporate bonds and credit default swaps.26 For example, assume a 10 percent cost of capital and further assume a margin of 5 percent on the credit default swap trade, and a margin of 25 percent on an investment-grade corporate bond (which was a typical level during the crisis). he direct efect of this margin diference between credit default swaps and corporate bonds is 10% × (25%-5%) = 2%, which is close to what was observed empirically during the crisis. Arbitrageurs oten invest other people’s money, resulting in a principal/ agent problem that is exacerbated in market downturns. If liquidity providers face external capital shocks, they become liquidity demanders, leading to a situation in which there are only sellers and no buyers in the market. Rather than increasing investment levels when prices dip below fundamental values, they may sell cheap securities, causing prices to decline further. As a result, liquidity spirals could cause prices to drop. New capital typically arrives slowly because information barriers separate investors from money managers, who face the prospect of investor redemptions as a result of the decrease in the value of managed funds. he increase in margin requirements further increases the cost of raising more capital.27 It is costly to maintain dormant capital, infrastructure, and talent for long periods of time, while waiting for proitable opportunities. Other agents may lack both the infrastructure and information to trade.28 Markets become highly illiquid when liquidity providers are constrained and traders demand higher

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expected returns as compensation for this lack of liquidity.29 he result is that proit opportunities for unconstrained irms can persist for months. hese observations are evidence that real-world frictions impede arbitrage and that the “speed of arbitrage” needed to restore the market depends critically on the availability of capital to participants. Limitation of asset pricing models Asset illiquidity raises a number of key asset pricing issues. With virtually no trades and no market prices in some markets during the crisis, market participants had to rely on models to assess valuations—but the models had been developed during a time of liquid markets and had not been tested in adverse conditions, casting doubt on the accuracy of their valuation estimates. We next discuss some of these limitations due to naiveté about particular aspects of market liquidity that afect asset prices. Nonmarketability of illiquid assets

Longstaf explains the risks created by asset illiquidity as nonmarketability of the asset. Illiquid assets cannot be bought or resold immediately.30 he implications of illiquidity and nonmarketability of assets were brought to the forefront during the crisis when markets for securitized products ground to a halt. he complexity of many structured securities combined with the inadequate information and disclosure that typically accompany trading in OTC markets caused markets to be more illiquid than they would otherwise be. hen-Chairman of the Federal Reserve Ben Bernanke observed in a speech at the Economic Club of New York on October 15, 2007, “Moreover, in the absence of an active syndication market for the leveraged loans they had committed to underwrite and without a wellfunctioning securitization market for the nonconforming mortgages they had issued, many large banks might be forced to hold those assets on their books rather than sell them to investors as planned.”31 Hedge fund investors were especially afected and let with completely illiquid assets as many funds suspended redemptions. he trade price of a liquid asset can be greater than the simple present value of cash lows. As highlighted by many examples in this chapter, the

36

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nonmarketability of an illiquid asset reduces its value to much less than that of a liquid asset with the identical cash low dynamics. A recurring theme is that it is not directly dividend cash low that matters but rather the consumption steam that asset ownership generates. Furthermore, liquidity is not symmetric, and has the efect of making liquid assets more valuable and illiquid assets less valuable relative to the equilibrium prices. he inluence of marketability of a security on asset pricing demands attention ater the experience of the 2007–2008 inancial crisis, but it is not a new idea. In 1959, Professor Lawrence Fisher presented an important hypothesis about the determinants of [the] yield spread, also referred to as the bond risk premium.32 Fisher showed that the average risk premium on a corporate bond depends on two factors, the creditworthiness of the lender (or the default risk) and the “marketability” or liquidity of the security. Empirical studies provide considerable evidence that diferences in liquidity can have major efects on the pricing of corporate bonds or, equivalently, on their required returns. Trading is costly to investors, and rational investors demand compensation for bearing liquidity (trading) costs. he price of corporate securities changes in response to changes in liquidity due to the increase or decrease in the return demanded by investors. Accordingly, the liquidity of a company’s bonds can directly afect capital structure decisions as well as the timing of debt and equity issuances. A case in point is the reinancing risk posed by short-term debt, which we will discuss in more detail in chapters 5 and 6. The importance of correlation risk

Most market participants recognized even before the crisis that correlation increases during a crisis, that is, markets that are seemingly unconnected in normal times can become connected in times of inancial stress. However, the widespread efects of this increased correlation were surprising. During the recent crisis, many companies simultaneously defaulted, were downgraded, or experienced severe capital constraints. he demise of the subprime market provided an exogenous market shock, which had dire consequences. An interesting analogy of this dynamic is the horizontal oscillations of the Millennium Bridge of London

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37

that forced the bridge to close for further tests three days ater its opening to the public.33 Adverse shocks reduced the amount of capital available to inancial intermediaries, which lowered their ability to make deep liquid markets in which trades could be executed at narrow bid-ask spreads with small price impacts. As liquidity in the market worsened, trading falls and the short-term cash inlows of these institutions drop too, since most of their proits arise from market-making revenues. he worsening of short-term cash inlows of intermediaries, and in turn, their funding ability, limits their liquidity-provision role even further, giving rise to a downward spiral and a sudden drop in both funding liquidity of intermediaries and the market liquidity they provide. To summarize, if an asset shock is great enough that the capital position of a suiciently large number of intermediaries is rendered constrained (or close to being constrained), then there may be a sudden drying up of both funding liquidity and market liquidity. hese dynamics were ampliied by a herding behavior of market participants, such as increased margin calls and the need for the forced sale of assets. his link between funding and market liquidity risks implies that prices in capital markets efectively exhibit two “regimes.” In a normal regime, intermediaries are well capitalized and liquidity efects are minimal: the prices of assets relect fundamentals and no (or little) liquidity efect. hus, the correlation across asset prices in these times is also driven primarily by the correlation in the fundamentals of underlying entities or risks. In the illiquidity regime, intermediaries are close to their funding or capital constraints, and prices now relect the “shadow” cost of capital to these intermediaries, that is, the cost they sufer from issuing an additional unit of funding capital to undertake a transaction. In economic parlance, there is “cash-in-the-market” pricing, and the liquidity position of market participants in a particular security afects the price of that security. Since this liquidity efect (illiquidity discount) is related to intermediaries’ capital rather than to the fundamentals of the security, it afects the prices of securities traded by these intermediaries across the board, inducing a correlation in securities’ market prices over and above the one induced by the fundamentals.34

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Appendix Ackermann, J. 2008, “he Subprime Crisis and Its Consequences,” Journal of Financial Stability, Vol. 4, pp. 329–337. Acharya, V.V. and M. Richardson, 2009, “Causes of the Financial Crisis,” Critical Review, Vol. 21, Nos. 2–3, pp. 195–210. Gerardi, K.S., A. Lehnert, S. M. Sherland, and P. S. Willen, “Making Sense of the Subprime Crisis,” 2009, Federal Reserve Bank of Atlanta Working Paper 2009–2 (February). Gorton, G. 2008, “he Panic of 2007,” Prepared for the Federal Reserve Bank of Kansas City, Conference, Jackson Hole, WY. Gorton, G. 2009, “Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13, 2009. Gorton, G. 2009, “Information, Liquidity, and the (Ongoing) Panic of 2007,” American Economic Review, Vol. 99, No. 2, pp. 567–572. Gorton, G. 2008, “he Subprime Panic,” National Bureau of Economic Research, Working Paper 14398. Gorton, G. B. 2010, Slapped by the Invisible Hand: he Panic of 2007 (Oxford: Oxford University Press). Gorton, G. B. and P. He, 2008, “Bank Credit Cycles,” Review of Economic Studies, Vol. 75, No. 4, pp. 1181–1214. Gorton, G. and A. Metrick, 2010, “Haircuts,” Federal Reserve Bank of St. Louis Review (November/December), pp. 507–520. Gorton, G. and A. Metrick, 2012, “Securitized Banking and the Run on Repo,” Journal of Financial Economics, Vol. 104, No. 3, pp. 421–560. Gorton, G. and A. Metrick, 2012, “Getting Up to Speed on the Financial Crisis: A One-Weekend-Reader’s Guide,” Journal of Economic Literature, Vol. 50, No. 1, pp. 128–150.

3

Market Structures and Institutional Arrangements of Trading

Introduction Markets match buyers and sellers, and enable the formation of security prices. he viability of a market therefore depends on how well buyers and sellers are matched and how accurately the trade price evolves. Matching implies the provision of liquidity. In classical inance theory, this conjures an image of the Walrasian auctioneer letting the hammer down on the inal auction price. In real-world inancial markets, the role of the auctioneer is fulilled by the market maker or inancial intermediary. But liquidity also arises from other aspects of the trading mechanism that are determined by the institutional framework and structure of a particular market, which in turn are determined by rules and regulations that govern trade and interaction between market participants. Price formation involves the incorporation of new information into security prices. Price formation necessarily requires us to consider the tension between participants with information (“informed traders”) and participants without information (“uninformed traders”). On the one hand we have the institutional framework that determines how information becomes public, and on the other hand we have the fundamental features of the particular security that are more or less revealing about its true value. Money is the most liquid asset, and the cost of using money to transact is typically zero. he “market” for money is liquid and facilitates orderly and immediate transactions because money has a unique characteristic: the price of one dollar is always equal to one dollar. here is therefore no uncertainty about a dollar’s fundamental value. Residential real

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estate, in contrast, is relatively illiquid, precisely because of legal impediments involved in buying or selling homes, such as mortgage origination fees and title insurance, appraisal costs and the cost of home inspections. In addition, a relatively limited number of buyers for a particular home prevent instantaneous transfer between buyers and sellers. Financial assets such as stocks, bonds, and derivatives fall somewhere between these two extremes—where and how these securities are traded depends on the process of trade and the institutional arrangements and regulatory policy enabling this process. In other words, it depends on the underlying structure of the market. he inancial system is made up of the central banks, dealer banks, money market funds, major institutional investors, repo clearing banks, over-the-counter (OTC) derivatives, central clearing parties, and exchanges connected via a complex, constantly evolving set of institutional arrangements that allow participants to interact competitively to trade. A market can occupy a physical location such as an open outcry trading loor. A market can also be an electronic access network in which market makers use telephones or screen-based systems to arrange bilateral trades with each other and with customers, or it can be totally machine operated, as is prevalent in high-frequency trading. he connectors include lending facilities ofered by central banks to each other and to dealer banks, tri-party repo and clearing agreements, OTC derivatives, master swap agreements, prime brokerage agreements, and settlement systems arranged through Fedwire, operated by the Federal Reserve Banks, the Clearing House Interbank Payments System (CHIPS), CLS Bank, the Depository Trust Company, and other major custodians and settlement systems. he inancial crisis of 2007 to 2009 exposed many weaknesses in the structure of inancial markets and heralded in a new era of regulatory initiatives. In addition, rapid advances in technology, enforced or enabled by regulatory changes, led to widespread, automation and electronic communication systems that paved the way for the electronic trading systems that are challenging the dominance of loor-based exchange trading. High-frequency trading, or the use of powerful computers to analyze and execute trading opportunities at speeds of nanoseconds, which existed as

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a computing experiment only ive years ago, now accounts for 60 percent or more of trades in equities and futures markets in the United States. However, even though the underlying rules, processes, and roles of different agents are changing, the fundamental role of the market has not changed. In its most basic form, a market is an aggregation of buyers and sellers who interact according to a set of rules that determine what they can do, how fast they can do it, and what information is public versus private. he increased reliance on computerization and artiicial intelligence in inance has fundamentally transformed the inancial markets into faster, larger, more global, more interconnected, and less human entities than ever before. Our objective in this chapter is to further our understanding of market liquidity by looking at market structures. Our treatment investigates several distinct but related building blocks, such as the role of institutional arrangements, the role of technology, and the importance of capital. We explore questions such as how institutional arrangements and features of a particular structure either enhance or inhibit market liquidity, and how new capital requirements are changing the process of inancial intermediation.1 Market microstructure insights into security price formation In an eicient market, security prices fully relect all available information,2 and will accordingly change only in response to the arrival of new information.3 Market eiciency is a powerful concept of inance theory, it represents a market in which “prices provide accurate signals for resource allocation that is a market in which irms can make production-investment decisions, and investors can choose among securities that represent ownership of irms’ activities under the assumption that security prices at any time ‘fully relect’ all available information.”4 his hypothesis convinces us of the usefulness of security prices, but it does not explain the process of how information is being incorporated into security prices. he Walrasian auctioneer provides the simplest characterization of price formation. According to this framework, a ictitious auctioneer

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orchestrates the price formation process in a setup without any time constraints. Traders submit and revise their orders until the auctioneer inds a market price that allows all purchase and sale orders to clear. his market-clearing price is the equilibrium price or fundamental value of the security. Economist Harold Demsetz5 expanded the role of the single Walrasian auctioneer to several market makers and explained how the need for immediacy can afect price formation. Demsetz argued that, while a trader willing to wait might trade at the single price envisioned in the Walrasian framework, traders not wanting to wait have to pay a price for immediacy. he price for immediacy comes in the form of compensation to the market maker, who has to stand ready to execute trades upon receipt of an order.6 his results in two equilibrium prices, the bid price, at which an immediate sale can be executed, and the ask price, at which an immediate purchase can be executed. he size of the spread between the bid and ask prices, which is oten used as a measure of market liquidity, depends, among other factors, on the structure of the market and on the number of participants competing for orders. A case in point is the reduction in bid-ask spreads on the National Association of Securities Dealers Automated Quotations (NASDAQ) following the introduction by the US Securities and Exchange Commission (SEC) of two regulatory changes in 1997. he limit order display rule forced NASDAQ dealers to execute or display any customer’s limit orders better than their own, and the “quote rule” required dealers trading in multiple venues to make their best quotes available to the public. Demsetz’s replacement of the Walrasian auctioneer with market markers also introduced the role of inventory in price formation. he market maker or price-setting agent uses prices to balance supply and demand across time. Order low is uncertain, which exposes market makers to inventory risk because they may not necessarily be able to unwind a position immediately. As the market maker accumulates a long position in a security, he is exposed to the risk of a price drop, while a short position exposes him to the risk of a price increase. If a market maker is risk averse, his quotes should depend directly on his exposure to risky inventory. he size of the bid-ask spread should, in principle, be related to inventory

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holding costs. Empirical evidence does not support inventory efects in inancial markets.7 An alternative framework to price formation looks at information revealed by order low. If some traders have superior information about the underlying value of a security, their trades can reveal information about the underlying value and so afect the behavior of prices. his “learning problem” has been explained by Easley and O’Hara, two esteemed professors from Cornell University, as a Bayesian learning process.8 In the Easley and O’Hara setup, potential buyers and sellers trade with market makers. Market makers are competing with each other for order low and must set prices at which they will buy or sell any quantity of a security. he Easley and O’Hara setup adopts the actors proposed by Albert Kyle: informed traders, the market makers, and uninformed noise traders who transact randomly.9 Noise traders camoulage the activities of informed traders, whose transactions are organized in such a way that their private information is relected gradually in market prices.10 Market makers have a prior belief about the true value of a security. Traders ask competing market makers for their price-quantity quotes. he trader then either does not trade, takes the best quote for the quantity he wants to trade if he is uninformed, or takes the proit-maximizing quote if he is informed. Informed traders observe some data and update their expected value of the security to be either higher or lower than the market makers’ prior belief of the security value. he revealed information inluences the informed trader’s desired trading quantity, with good news causing the trader to buy and bad news eliciting sell orders. Assuming that uninformed traders are equally likely to buy or sell, whatever the information might be, the market makers then update their quotes conditional on the type of trade. he posterior then becomes the new prior, more data are observed, and the updating process continues. In information-based models, the solution to this learning problem determines the prices set by market makers. he ask price equals the expected value of the security given that a trader wishes to buy. he bid price is deined similarly given that a trader wishes to sell. An important characteristic of these prices is that they explicitly depend on the probability of a sale or a purchase. Good news

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will result in an excess of buy orders as informed traders decide to buy. Likewise, bad news will result in an excess of sell orders as informed traders decide to sell. Information-based models explain the bid-ask spread and provide insight into how the price change relates to order low. In “perfect” and complete markets, with comprehensive information available to all market participants, securities can be traded at their fundamental value. Markets are not perfect. Depending on the institutional arrangements of a trade, there are information asymmetries whereby borrowers (issuers of securities) know more about the risks than lenders (or buyers of securities). So market participants may be reluctant to trade in those securities whose characteristics and behavior under changing economic conditions are not well known. For example, the price of Apple stock is much easier to discover than the price of tranche B of the asset-backed security (ABS) issued by shelf X. In times of distress, when uncertainty increases, it may be impossible to determine the true value of securities in more opaque markets, due to a lack of trading. Market liquidity is inversely related to the degree of information asymmetry prevailing among economic agents. his phenomenon rests on the observations of 1970 Nobel laureate George Akerlof, who proposed that it is the diiculty of distinguishing good quality from bad quality that is the inherent problem. As explained in Akerlof ’s celebrated theory on the market for bad used cars, or “lemons,” a market may altogether disappear (the most extreme form of illiquidity) if information is suiciently asymmetric.11 Given that some traders have superior information, prices along the adjustment path may not fully relect all the information from public and private sources (i.e., be strong-form eicient) and there can be great differences in the speed with which prices move toward full information. But prices ultimately do converge to their true, full-information value, so markets are strong-form eicient in the limit. Overview of structural features of market design and their effect on market liquidity How can we assess whether a particular market structure enhances market liquidity? As a general matter, a market is considered eicient in fulilling

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its role of providing liquidity if it incorporates new information quickly and accurately into prices. he degree of information asymmetry between suppliers and demanders of liquidity determine market liquidity.12 But how do we quantify this further? A working deinition oten used in market microstructure discussions identiies the features of a liquid market as tightness of bid-ask spread, depth, and resiliency. Tightness of bid-ask spread measures the cost of a reversal of a position at short notice. Market depth indicates the volume of transactions that may be immediately executed without introducing slippage to the price, and resiliency refers to the speed with which prices revert to their equilibrium level following a random shock to transaction low. Market depth and resiliency indicate the market’s ability to absorb signiicant volumes without adverse efects on prices. Market structures necessarily involve a trade-of between these various dimensions. For example, greater competition among institutions providing market-making services can reduce the bid-ask spread and thereby improve tightness. But lower proitability for market making can lead to a withdrawal of capital and a reduction in the size of transactions that can be absorbed (reduce market depth). Liquidity depends on how the market is structured, but in turn liquidity needs also dictate the structure of the market. We next review some of the critical structural issues in the creation of liquidity. Adverse selection and asymmetric information To understand the link between liquidity and adverse selection, we again look at the theory of market microstructure for insight. Professors Lawrence Glosten and Paul Milgrom created a well-accepted model that showed that market makers in a competitive environment widen the bidask spread beyond what it would otherwise be to recover from uninformed traders what they lose to informed traders.13 his so-called adverse selection component induces a wider bid-ask spread to compensate market makers for the possibility of asymmetric information.14 Market liquidity is likely to be enhanced if information about the assets’ value is distributed symmetrically between intermediaries and potential

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buyers and sellers. Wide bid-ask spreads set by intermediaries can oten be interpreted as relecting asymmetric information. How does adverse selection afect market structure? To illustrate this link, consider the current market setting in Europe where, unlike in the United States, the regulator does not explicitly prohibit trades to execute at prices that are inferior to the best available price across all relevant trading platforms.15 Examples of multimarket trading are French and German stock listed on their respective home markets (Deutsche Börse and Euronext) and on Chi-X, the irst multilateral trading facility (MTF). For these stocks, liquidity providers post quotes in two separate platforms, the Deutsche Börse and Euronext, referred to as the primary market and the lower-cost Chi-X, an MTF. While the primary market is accessible by all agents, trading on the MTF requires a so-called smart order routing system that is available to a subset of agents. Empirical results show that the “smart routers” are also more informed, so trades on the MTF carry signiicant more private information.16 his implies that liquidity providers on the MTF incur a higher adverse selection risk precisely because an important fraction of the uninformed order low is held captive in the primary markets. Primary markets display a better quote than the MTF Chi-X’s, or, put diferently, primary markets’ best bid-price will improve or match the bid on Chi-X’s due to the excess adverse selection risk on the MTF. he example illustrates how the competitiveness of alternative trading platforms is hampered by the concentration of uninformed order low in primary markets. Transparency Transparency captures the amount of information about the trading process that is available to market participants. Information in this context can be prices, quotes, volumes, order low, and the identity of market participants. Consider a retail investor interested in buying a share of Apple for his investment account.17 He could check the latest prices available on Yahoo Finance, but these are delayed, so uncertainty about the exact price he will get at execution remains. Alternatively, the investor could

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subscribe to a real-time feed such as ICAP or Reuters and learn the price of the most recent trades. But such subscriptions are costly and do not provide quotes for the next trade. What if the investor wants to buy a corporate bond or a structured product? Most corporate bonds and structured products are traded in the OTC market, which is at the opaque end of the spectrum—no information on quotes from market makers is made available. he lack of transparency afects not only retail investors but also mutual fund managers or brokers, who oten do not have a complete picture of the market. A critical component of price discovery and a hot-button regulatory issue is for market participants to have access to suicient information about market conditions. It is useful to divide transparency into two dimensions. Pretrade transparency relates to the quantity and quality of information on the trading process that is made available to market participants. Pretrade information such as bid and ask quotations, trade prices, order low, and volumes is useful to traders, who may use these in the development of trading strategies and to improve their estimates of a security’s value. Posttrade transparency relates to the extent to which posttrade information such as execution time, volume, and price is disseminated among brokers, customers, and the public and to the speed of dissemination in real time versus delayed feed. A reason for the crucial role of transparency in trading is because it afects how trading gains are distributed between the investors and the market makers. Delays in reporting usually favor the market makers and potentially also large traders, while transparency favors the other market participants and the markets as a whole. Transparency about quotes, orders, and traders’ identities generally improves market liquidity for uninformed traders. Many exchanges implement electronic limit order books that ensure a level playing ield for liquidity suppliers in order to mitigate market power. In 2002, the New York Stock Exchange (NYSE) started disseminating electronically its limit order book, which increased transparency and enabled investors to monitor and cancel their limit orders. Greater transparency of the open limit order book reduced the informational advantage of the specialist and attracted more limit orders in the book. his initiative increased

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the liquidity displayed on the NYSE.18 In contrast, if the dealer market becomes too transparent, dealers may lose their incentive to intermediate given their ixed costs and the risk of adverse selection by informed traders. A suiciently large increase in transparency in the OTC market could potentially reduce trading opportunities for investors.19 Transparency depends not only on the availability but also on the cost of information. he degree of transparency also varies by security type. Realizing the positive feedback between greater transparency and greater market liquidity, the Financial Industry Regulatory Authority (FINRA) developed the Trade Reporting and Compliance Engine, or “TRACE,” in 2004. TRACE is an automated system that, among other things, accommodates reporting and the dissemination of transaction reports. Execution costs in the municipal bond markets fell by half ater posttrade information was disseminated through TRACE. Liquidity profile of securities Each inancial security has a unique “liquidity proile” that determines the ease with which its salient features can be credibly communicated to a large investor base. he more homogeneous or standardized the security, the more likely multiple buyers and sellers will be found, which will greatly increase trading volumes and thus market liquidity. For instance, futures contracts are standardized across various features of the underlying asset or commodity in order to attract heterogeneous buyers and sellers. he maturity date, a par or notional amount, a speciied deliverable item with transparent characteristics, and an established trading unit, or “tick size,” are all relevant standard features of such a contract. At the other end of the spectrum are bespoke securities traded in the OTC market, which is designed speciically to suit the buyer and seller in a way that personalizes the transaction to the investment proile or hedging needs of the participant. Examples include securitized products such as ABS, collateralized debt obligations, and exotic derivatives such as variance swaps. hese products are oten not intended to be traded in a broader market, but are meant to be held until maturity by the original buyer.

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Institutional arrangements play an important role in determining the liquidity proile of securities. Oten, the characteristics of an asset that inluence the degree of liquidity are determined before the irst trade in a security takes place. Standardization is also a critical consideration in the debate on central clearing mandates. Standardization of product features is a precursor for exchange-like execution, central clearing, and greater transparency of OTC markets. Standardization increases the notional turnover, which drives down the trading cost for the entire asset class. For example, Figure 3.1 shows the estimated average cost per trade versus the average number of daily trades for a spectrum of securities.20 he empirical results shown in Figure 3.1 clearly indicate the much greater trading volumes for commoditized products such as cash equities or listed derivatives when compared with bespoke products in the OTC market. It is also important to distinguish between primary and secondary market liquidity because high volumes in primary markets do not necessarily imply liquidity in the secondary market. Particularly, the markets

Estimated Average Cost Per Trade ($)

1000 Volume Increase = 5x Cost Reduction

OTCD-Equity OTCD-Commodity OTCD–Rates CDS

100

10

OTCD-FX

Cash-Rates FX-Spot

Listed Derivatives

1

Cash-Equities

0.1 1

10

100

1000

10000

100000

Average Number of Daily Trades (000’s) Figure 3.1 Volume versus cost per trade by security class (OTCD refers to OTC derivatives). Sources: ICAP, BIS, WFE, TABB Group.

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for customized credit derivatives and collateralized debt obligations are highly tailored to meet speciic investor needs, which make them rather illiquid in the secondary market. he lack of secondary market liquidity may not be a major problem if the users, oten long-term investors, desire the credit exposure and do not engage in active trading. However, an investor wishing to unwind or modify the position may have to rely on the initial arranger of the transaction, who may not be willing or able to provide liquidity under stressed market conditions, or may do so only at signiicant depressed prices. Market structures Market structures are very diverse and constantly evolving due to regulatory reform and technological advances. An ininite number of rules and trading protocols determine the actions of market participants, for example, how to place an order, how much information is available about other participants’ actions (actions can include either all or any of the participants’ quotes, order low, or transaction prices), what is the protocol for matching buy and sell orders, and whether the execution price difers depending on who placed the order. Institutional arrangements play a critical role in the design of markets and ultimately in market liquidity. We illustrate this by an in-depth look at representative market structures. On the one end of the spectrum we have the “quote-driven” dealer market, as is typical of trading in the OTC market. Corporate bonds in the United States and structured securities such as collateralized mortgage obligations and residential mortgage obligations, emerging market debt, currencies, some derivatives, and certain equities are traded in the OTC markets. Final investors can only trade at the bid and ask quotes posted by specialized intermediaries, also called dealers or market makers. On the other end of the spectrum we have the “order-driven” auction, or limit order market, as is typical of trading on centralized exchanges. Derivatives such as Eurodollar futures, options on Eurodollars, and most equities are traded via electronic limit order books on centralized markets such as the NYSE (NYSE-Euronext), London Stock Exchange (LSE),

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Deutsche Börse, LIFFE, CME Group, and so forth. An exchange typically runs a limit order market, in which inal investors interact directly. Buy and sell orders are consolidated in a central order book and executed according to time and price priority. he highest buy orders and the cheapest sell orders are more likely to be executed. he NYSE priority rules determine the coordination of prices in these trading mechanisms. he NASDAQ on the other hand is a strictly electronic exchange in which large investment companies buy and sell securities through an electronic network. Each market maker on the NASDAQ is required to give a two-sided quote, meaning they must state a irm bid price and a irm ask price that they are willing to honor. In the 1970s, most shares were listed on the NYSE, and all exchanges were loor-based auction markets. Nonlisted securities were traded in an informal dealer market, which became a formalized dealer market known as NASDAQ. It consisted entirely of dealers who made markets in securities that were not listed on any exchange by ofering to buy or sell shares as principals. Innovation and the SEC’s adoption of the Regulation Alternative Trading System (Regulation ATS) in 1998 formalized the development and registration of new trading systems.21 Key elements of the Regulation ATS were the registration of for-proit entities as exchanges, providing ATSs the option to register as exchanges and expanding the deinition of exchange to include ATSs that perform market functions. he implications of these regulatory changes were far reaching—exchanges became for-proit entities, which basically put them in direct competition with each other for market order low. Entities acting as electronic communication networks (ECNs), which are ATSs that make their quotes available to the public, could register as exchanges under the new regulations. hese ECNs were fully electronic, and therefore competed for the same order low as the NASDAQ, the only incumbent electronic exchange at the time. Over time, dealers were replaced by order-matching computer systems. So ierce is the competition among trading venues that NASDAQ trades less than 25 percent of NASDAQlisted shares, and most of the rest are traded on the ECNs.22 Historically, the NYSE was the dominant trading venue for NYSE-listed securities, with almost 80 percent of all trading in NYSE-listed stocks taking place

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on the NYSE. Due to regulatory changes and technological advancement, however, less than 30 percent of NYSE-listed stocks traded on the NYSE during November 2014. he SEC’s regulatory decisions, in particular the trade-through rule, are an important factor in these developments. he trade-through rule requires that orders to buy or sell securities listed on any registered stock exchange be sent for execution to the market in which the best price is posted. Assume that an institutional buyer wants to purchase shares of an NYSE-listed security, which has been posted for sale on an ECN at $30. At the same time, an ofer to sell 100 shares of the security at $29.50 is posted on the loor of the NYSE. Under the trade-through rule, the order must be sent to the NYSE to clear. Because the NYSE is the largest and most liquid market for these securities, its prices tend to be the best, and most orders low there irst. hus, the trade-through rule has reduced the likelihood that any serious competition for the NYSE will arise, thereby ensuring the dominant position of the NYSE in trading NYSE-listed securities. Since NASDAQ was a dealer market, it was not subject to the tradethrough rule and was thus vulnerable to competition from ECNs that ofered superior services or other advantages. ECNs ofered superior trading over the NASDAQ dealer market, which particularly beneited institutional investors, who could execute trades at lower cost and achieve better overall pricing on the ECNs. Eventually, in a bid to remain a viable market, NASDAQ sought the approval of the SEC to become a privately owned electronic market, enabling them to compete with the ECNs for market share in NASDAQ securities. he SEC’s adoption Regulation National Market System (NMS) in June 2005 provided the regulatory framework that governs the current shape of the US equity markets.23 he SEC designed Regulation NMS to establish extensive baseline rules to govern how trading centers and broker-dealers interact with the rest of the market when routing orders for execution, displaying quotations, and disseminating market data to the public.24 Regulation NMS proposed to apply the trade-through rule to the NASDAQ market, to which it had not been applicable before, but it also allowed institutional investors to opt out of the trade-through rule on a trade-by-trade basis. Regulation NMS increased competition between

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exchanges with a growing number of equity exchanges and other venues. he volume of shares of stocks trading on the NYSE decreased from 78 percent to less than 30 percent between 2004 and 2014. As of the third quarter of 2014, other venues, particularly NASDAQ and some ECNs, executed more than 70 percent of volume in NYSE stocks.25 In our discussion of liquidity within representative market structures in the next sections, we revisit some familiar themes of aggregation of buyers and sellers, speed of transacting, information sharing and so on. For example, the speed of transacting is greatly inluenced by the existence or absence of an intermediary and the trading venue. he congregation of buyers and sellers, either physically or electronically, determines the ease of trading—it is much easier to match buyers and sellers in an exchange-traded environment, with well-established methods of recording and publishing prices, than in the OTC market (an exception is foreign exchange markets, in which OTC spot, forward, and option trades exceed their exchange-traded equivalents). Each of these market structures poses a unique set of challenges to policy development. Diferent causes of market illiquidity call for diferent remedies. If order processing costs are high, upgrading technology or rules that would open competition among trading platforms may be appropriate, but if adverse selection causes illiquidity, actions against insider trading and timely release of trade-related market information to all participants may mitigate the advantage of informed traders. Key features of the over-the-counter/dealer market We ind a clear distinction between participants who supply liquidity and participants who demand liquidity in the dealer market. In a typical setting, dealers supply liquidity to investors who demand liquidity. Investors request quotes from a dealer or multiple dealers, typically over the phone or using an electronic access network. Dealers quote bid prices to customers interested in selling securities and similarly quote ask prices to customers wanting to buy securities. In the corporate bond markets and in some foreign exchange markets, dealers provide quotes only upon request. However, in other markets such as the quote-driven NASDAQ

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Stock Market, dealers must continuously post irm prices at which they will trade. In both cases, dealer proit depends on the volume of trade it handles and on the diference between the bid price and the ask price. Dealers must attract order low, which motivates them to quote aggressive prices as trades are executed only with the dealer who provides most competitive bid or ask. he dealer typically acts as the counterparty to all customer trades. No price priority is enforced. he dealer market is considered bilateral. A transaction is privately negotiated between two counterparties involved in the inal transaction— the investor and the market maker with the most competitive quote are privy to the terms of the trade. For this reason, the OTC market is considered opaque. Many dealer markets, such as the US corporate bond market, do not ofer any real-time information.26 In the currency market, Bloomberg’s and Reuters’ screens provide information on indicative quotes, but dealers are not committed to execute trades at those prices. Dealers execute trades with inal investors, but they can also trade directly with each other in the interdealer market. Some dealers also act as brokers, an execution agent who routes buy and sell orders between the inal investors and other dealers. Another segment of brokerage is the interdealer-broker, who is a specialized execution agent for trades in the interdealer market. Dealers such as Goldman Sachs are referred to as broker-dealers because they ofer both brokerage and market-making services. Brokers and dealers are collectively referred to as the “sell side” of the securities industry because they facilitate trade execution in the market. Final investors, which could be households, intuitional investors, irms, and governments, are referred to as the “buy side.” Search and bargaining in OTC markets he OTC market has two distinct features: bilateral trade that is privately negotiated between market makers and their customers, and the responsibility of market making resting solely with dealers. hese features signify several interesting features of market liquidity. An important role of dealers is to “discover” the price that will produce a two-way order low. In order to fulill their role as liquidity provides,

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dealers typically have inventories of the securities they trade that expose them to the risk of a change in the prices of securities and hence a potential loss on the value of their inventories. A dealer could reduce his exposure to market risks by hedging these positions, using an ofsetting customer transaction or by transacting with another dealer. Some past academic studies consider the management of inventory and inventory risk to be a major determinant of the bid-ask spread in the dealer market.27 More recent academic studies consider search cost in locating counterparties and the bargaining power of dealers and investors to be critical components of market liquidity.28 he absence of a centralized market implies that an investor who wants to buy must search for a seller, incurring opportunity or other cost until he inds one. Similarly, an investor who wants to sell a security must ind a counterparty who is willing to sell the desired security and the desired quantity. Search costs are partly attributed to institutional arrangements in the dealer market. For example, OTC derivatives are typically executed between a dealer and the inal investor, only ater the parties have signed a master agreement that conforms to standards set by the International Swap and Derivative Association (ISDA). he master agreement deines, among other things, the collateral requirements as well as the obligations of the two counterparties in the event that one of them defaults. Ater the initial signature, the posted collateral may be adjusted periodically to relect changes in the market values of the derivative contracts between the counterparties. Delays in inding a counterparty could arise because of delays needed to verify credit standing, or to arrange trade authorization and inancing or the time necessary to familiarize investors with contractual terms and product type.29 In some cases investors negotiate price concessions if they need to trade quickly, but this typically lead to noncompetitive execution prices. In general, the search process is time consuming, which increases the speed with which transactions can be executed. he search process is also costly, which manifests as inancing costs or opportunity costs. Once a counterparty is located, the price is bilaterally negotiated. he dealer typically sets the bid-ask spread based on the customer’s perceived outside options of inding a diferent counterparty for the trade. According to a study by Professor Darrell Duie et al., the execution price

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relects the participants’ outside option to ind another counterparty who can facilitate immediate execution.30 he feasibility of outside options from the investor’s perspective implies that they may be able to buy the same security from a diferent dealer at a lower price. Similarly, from the dealer’s perspective it implies that the dealer may be able to sell the same security to a diferent investor at a higher price. Smaller investors oten have an account with only one or a few dealers, which gives them less bargaining power than larger investors, such as institutional fund managers, who have access to multiple dealers at any given point and who can typically trade with multiple dealers. Consider the US market for municipal bonds as an example. he large amounts and high frequency of trade by institutional investors relative to retail investors gives them greater bargaining power, which is evident in the more competitive execution prices of institutional investors. Bargaining power also introduces a trade-of between execution speed and price. An investor with a large order can ask a dealer to quote a price at which he is willing to execute the whole order. his price guarantees immediate execution of the full order, but it may be suboptimal to the price the trader would obtain by splitting the order among several dealers over time.31 Market liquidity also depends on the presence of a suicient number of counterparties and their willingness to trade. he latter depends on investors’ expectations regarding price developments and also their risk aversion at a given time, as well as the information available (e.g., on issuers’ creditworthiness). A “good equilibrium” of regular liquidity therefore presupposes heterogeneous expectations and behavior, ensuring the execution of orders irrespective of the transaction direction. It then only takes a hint of doubt creeping into market operators’ minds to radically change the market coniguration and trigger a liquidity crisis. he structural features of the OTC market are the bilateral negotiation of trade and the separation of agents who supply and demand liquidity. he size of the bid-ask spread and market liquidity more generally are determined by search costs, inventory costs, outside options, and bargaining power. Market participants require compensation for bearing the costs and the uncertainties of dealer market execution. hese risks should therefore afect security prices as we discuss in more detail in chapter 4.

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he structure of the OTC market place a central role in the distribution of trading gains between the buy-side investors and the sellside intermediaries, who can have very diferent views on how trading should be organized. Key features of limit order books he nature of limit order markets fundamentally difers from dealer markets in that orders are executed in a centralized market in which the buy and sell orders of participants are matched directly. he centralized marketplace can be the loor of an exchange or a virtual, electronic trading platform. Any participant can either supply or demand liquidity. Execution prices are determined using sophisticated auction procedures based on the time and price priority of orders. Price priority dictates that the highest price buy order and the lowest price sell order take precedence in execution. Time priority dictates a “irst in irst out” sequencing whereby older limit orders are executed before more recent orders. First movers at a given price are therefore rewarded for providing liquidity at that price. Participants execute trades by submitting limit orders, market orders, or a combination of both. he key diference between these two types of orders is the probability of execution and the price at which each is to be executed. he most common type of limit order speciies the price and a given amount of a security to be transacted. A buy limit order speciies the maximum price at which the trader is prepared to buy a stated amount of the security, while a sell limit order is the minimum price the seller will accept for a given amount. Since the speciic prices are either above the current ask price or below the current bid price, limit orders are stored in the order book, and a movement in prices is required for such orders to become active. Limit orders provide liquidity to the market, and it is possible for the trader to improve the prices in the market.32 However, limit orders face the risk of nonexecution—that is, unexecuted limit orders queue up in a limit order book and are in force until illed or cancelled. Limit orders also face adverse selection risk. If new public information arrives, limit orders can become mispriced and may be executed at a loss.

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Market Liquidity Risk

A market order only speciies the amount of the security and not the execution price. Assuming there is an outstanding limit order on the other side, a market order is immediately executed at the best price currently available in the market. he limit price and the size of the order book determine the market-clearing price, which implies that the same security can be traded at diferent prices. As explained by Bruno Biais, Larry Glosten, and Chester Spatt, authors of Market Microstructure, in an article published in the Journal of Financial Markets, “Another diference between call auctions and continuous trading is that in the former all trades are executed at a single uniform price, while in the latter, as orders walk up or down the book, and as the latter evolves, trades are illed at diferent prices.”33 A key strategic decision for participants in this market is the choice between submitting a limit order versus a market order because it determines both the speed of trading and the execution price. Terry Foucault suggests that volatility of the asset is an important determinant of the mix between market and limit orders.34 In a volatile market, the probability of mispricing an asset is higher, and so limit order traders require greater compensation for bearing mispricing risk. Limit orders quote relatively wide bid-ask spreads, which raises the cost of market order trading. One could argue that an increase in price volatility increases the relative proportion of limit orders. Electronic limit order books enable global participation in the order book. It is also feasible to implement complex algorithms that predeine priority rules using computers. Indeed, in recent years, there has been a general move toward open electronic limit order books. Electronic trading is rapidly replacing lively loor trading. In 2007, approximately 95 percent of trades and 85 percent of volume at the NYSE were handled by computer programs. Most equities and some derivatives are either pure electronic limit order markets or at least allow for customer limit order markets in addition to on-exchange market making. For example, Bank of America was one of the most actively traded equities on the NYSE during the last few months of 2014. Trading occurred on both the traditional NYSE customer limit order market and the NYSE Arca all-electronic exchange, with on average 8 percent of daily volume traded on the NYSE Arca.

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Another example is Facebook, which is listed on the NASDAQ but also on the NYSE Arca all-electronic exchange. During the last few months of 2014, an average of 9 percent of the daily volume of Facebook was traded on the NYSE Arca. he CME Group’s CME Globex platform is an open access marketplace that allows customers to participate directly in the trading process, view the book of orders and prices, and enter their own. CME Globex ofers global access to all major asset classes—interest rates, equity indexes, FX, agriculture, energy, metals, weather, and real estate.35 Globex accounted for approximately 90 percent of the exchange’s average daily volume of contracts traded in 2013. We expect this market model to develop further, which is consistent with the view that the electronic open limit order book is inevitable. Rather than a gigantic integrated order book, it is likely that several limit order books will coexist. Such coexistence is desirable, since, along with the competition among liquidity suppliers within one market, the competition across markets plays an important role in curbing market power and intermediation rents. Several exchanges have recently gone public, for example, Euronext and the London and Frankfurt Bourses. In contrast, the NYSE is not publicly held, but rather is owned by its members (specialists and brokers). Because of trading on a centralized market, illiquidity costs due to search and bargaining power do not afect trading, but other issues such as trade-ofs between the cost and beneits of limit orders versus market orders are important determinants of market liquidity. Liquidity in limit order books

Liquidity on an exchange depends critically on having a suicient number of balanced buy and sell orders—limit orders only execute if enough market orders arrive in the future to execute them plus all the additional limit orders already in the queue. In his classic paper, the economist Harold Demsetz argued that the cost of transacting declines with an increase in trading activity for that security.36 he order in the front of the limit order queue will be illed more quickly if the security is more active. In general, traders will be willing to raise the price if they are willing to buy and lower the price if they are willing to sell in order to get to the front of the

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queue. his dynamic efectively increases the bid-ask spread. However, if the security is active, in other words, if the frequency of transacting is high, then the cost of waiting in the trading queue will be lower. he bidask spread will be smaller because traders do not need to adjust their bid and ask prices to preempt positions in the trading queue. In equilibrium, longer waiting times for inactive securities translate into less market liquidity. A related aspect of limit order market liquidity is measured as the probability that enough limit orders will arrive to return the book to the minimum bid-ask spread before the next transaction. his is referred to as the resiliency of the order book. he behavior of traders in limit order books is further explained as follows: both patient and impatient investors use market orders when the spread is at its minimum, but only impatient investors use market order when the spread is wider. he importance of suicient volume becomes clear in the extreme case in which the limit order book is too thin for price discovery to happen. A limit order market can fail, even in the absence of the adverse selection problems that plaque the OTC market.37 he intuition for market failure is that if the limit order book is too thin, then price elastic market order submitters will scale back their market order submissions. However, as the endogenous distribution of submitted market order quantities shits toward zero, the probability of limit order execution falls, which, given ex-ante limit order submission costs, leads to fewer limit orders and, thus, a thinner book. If market order submissions are suiciently elastic, the limit order book may fail. Portniaguina et al. showed that the tick size is an important institutional feature that is relevant for preventing market failure. If the tick size is too small, it is easier for the specialist to undercut the book which, in equilibrium, makes the book thinner. he conceptual appeal of limit orders masks the complexity of the dynamic interactions and nonlinear payofs that can be generated. A limit order book does not have a unique, market-wide clearing price, but rather a sequence of matched pairs of buy and sell orders over time. Prices are formed as investors arrive and trade asynchronously. his market structure is fundamentally diferent from the Walrasian market, in which the execution price relects the clearing price of aggregate supply and

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demand. he prevalence of limit order markets validates the theoretical results of Glosten that limit order markets provide the maximal liquidity in the presence of adverse selection.38 he prevalence of order low from high-frequency traders in recent years, discussed in more detail toward the end of this chapter, raises important questions about the optimality of limit order markets.39 Institutional features such as tick size, trading volume, and the dynamics of order low are important market design considerations in a limit order market that should be considered in order to improve market liquidity and price discovery. Empirical results show that exogenous costs such as order processing costs account for over 80 percent of the bid-ask spread in limit order markets.40 Unlike asymmetric information costs, which depend on information revealed by the total trade of active investors across all markets, order submission costs are independent of what happens in other markets.41 Market liquidity: NYSE versus NASDAQ trading

he structure of the market has an efect on liquidity. We ind empirical evidence of the efect of structure on liquidity by comparing the bidask spread of transactions executed on the NYSE/AMSE auction market and transactions executed on the NASDAQ dealer market. Recall that traders who demand immediate execution, at the current bid price or current ask price, place market orders. he diference between the ask price and the bid price, or the bid-ask spread, measures the price concession paid for immediacy that was formalized in a 1968 study by economist Harold Demsetz.42 In the spirit of Demsetz, Professor Hans Stoll proposed the following cross-sectional regression model of bidask spread:43 s = ao + a1 log V + a2σ 2 + a3 log MV + a4 log P + a5 log N + ε , where s is proportional to the quoted half-spread deined as ½ (ask pricebid price)/P, V is the dollar daily volume, N is the number of trades per day, σ2 is the stock’s return variance, MV is the stock’s market value, P is the stock’s closing price, and ε is an error term.

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he rationale for these variables is the drivers of market liquidity in order books. Increases in volume, number of trades, and irm size raise the probability of locating a counterparty, thereby increasing the possibility of immediate execution. he stock’s return variable measures the risk of adverse price change that afects the supplier of liquidity, as discussed before. Price controls for the efect of discreteness is an additional proxy for risk in that low price stocks tend to be riskier. he quoted spreads are negatively related to measures of trading activity, such as volume. Spreads are also negatively related to stock price and positively related to a stock’s volatility. here are some diferences by exchange, but regardless of trading on the NASDAQ or NYSE, the liquidity measures are statistically signiicant measures of the quoted bid-ask spread. Stoll’s study found that liquidity is lower on the NASDAQ than on the NYSE. Dark pools A dark pool refers narrowly to an Alternative Trading System (ATS) that does not display bids and ofers in the public quotation stream. More broadly, a dark pool refers to sources of liquidity that are not relected in public quotes, such as dark orders on exchanges and internalization of orders by brokers and dealers. According to 2008 estimates, the volume percentage of dark pools has remained at approximately 20.44 While the organizational structure of dark pools is relatively new, the function of ofering shielded liquidity is not new. Trading in dark pools has been around for as long as there has been a market. Floor traders on the NYSE, for example, represent a manual market that can only be accessed by sending a buy or sell order to the loor. Dark pools provide a mechanism for large institutional investors who need to trade in substantial size without displaying their trading interest. Many dark ATSs exist as an attempt to service the trading needs of diferent types of investors and traders. An important regulatory aspect and a source of the increased cost of trading is the fragmentation of dark pools.45 As of the end of 2013, dark pools were really a disjointed network of individual alternative trading systems, each operated according to its own rules for price discovery

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and trading. We can categorize dark ATS into three groups based on how traded prices are formed. Systems such as IGT Posit, Liquinet, and Instinet are classiied as group one pools. Owners of group one pools act as agents for matching customer orders (as opposed to trading for their own accounts), and orders are executed at volume-weighted average prices from the lit markets. he second group operates dark pools as continuous nondisplayed limit order books, accepting both limit and market orders. Group two includes pools owned by major broker-dealers, including Credit Suisse Crossinder, Goldman Sachs Sigma X, Citi Match, Barclays LX, Morgan Stanley MS Pool, and UBS PIN. Unlike group one pools, group two pools derive their own execution prices from the limited prices of submitted prices. he third group includes high-speed systems such as Getco and Knight that act like fast electronic market makers that immediately accept or reject incoming orders. Participants in groups one and two usually act as trading agents to their customers. In contrast, participants in group three do not act as agents for other people but trade as principals for their own accounts.46 his large number of separately organized dark pools poses challenges for market participants. hese include the basic logistical task and cost of establishing connectivity to many diferent venues. he only way to know whether a dark pool is liquid, is to route an order to the pool. Routing this type of pinging order is a less eicient means of assessing liquidity than viewing centrally displayed quotes from multiple trading venues. A key cost of fragmentation for traders is the opportunity cost of being out of the market on one venue when searching for liquidity in other venues. Competitive forces seem particularly apt to address the problem of fragmented dark pools. he ultimate users of dark pools seem likely to pressure operators of the less successful pools to consolidate with other pools. High-frequency trading High-frequency trading broadly refers to automated trading that employs technology and algorithms to capitalize on very short-lived information gleaned from publicly available data using sophisticated statistical, machine learning and other quantitative techniques.47 High-speed

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trading had been technologically possible for many years, but it was legislative changes such as the US Regulation National Market System Law of 2005, known as “Reg NMS,” and the European Markets in Financial Instruments Directive, or “MiFID,” in force since November 2007 that enabled actual trading. he SEC designed the ITS order routing system to connect exchanges to the National Market System and to each other, which ofered remote access to market participants. hese changes in the market structure, combined with innovative inancial strategies, paved the way for a new era of proitable high-frequency trading. One of the most notable rules under Reg NMS is Rule 611, referred to as the “trade-through rule,” which was originally released by the SEC as the “order protection rule.” he objective of the rule was to ensure that investors’ orders are being executed at the best available price. A trade-through occurs when one trading center executes an order at a price that is inferior to the price of a protected quotation displayed at another trading center.48 he implications of this rule were that loor-based trading systems lost their primacy to electronic systems. In February 2015, the CME Group approved the closing of their historical trading pits in Chicago and New York.49 High-frequency trading is now the norm for trading inancial assets in electronic markets around the world—equities, foreign exchange, futures, or commodities. High-frequency traders provide not only the bulk of the volume in these markets but also most of the liquidity.50 Many irms that engage in high-frequency trading seek to end the day with little or no exposure to the market. While the speed of information lows and order lows is critical to high-frequency trading irms, a view of high-frequency trading as a faster version of the same old markets is too narrow. High-frequency trading represents a new paradigm for trading inancial assets based on “event-based time” (such as transactions or volume) rather than chronological time, relecting the fact that machines operate not on a time basis but rather on a volume basis. For example, a high-frequency trader will monetize accurate forecasts of e-mini S&P 500 futures volatility over the next 50,000 contracts, whatever the number of hours or milliseconds it takes to exchange that volume. High-frequency traders do not rely on forecasts of volatility over a chronological time horizon because they must control the volume of their inventory.51

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High-frequency traders exploit the ineiciencies in how markets operate. Such ineiciencies arise, for example, from tick size speciications, matching engine protocols, or latency issues in sending orders both within and across markets. In order-driven markets, bids and ofers never arrive simultaneously, so there is always a need for a liquidity provider, that is, the economic agent that bridges the mismatch in time between buy orders and sell orders. Historically, human market makers have illed this role of providing continuous quotes to help facilitate trading. With technology and regulatory innovation, markets and trading are faster, so the human market maker’s ability to quote efectively has become harder to accomplish. Machines and algorithmic processes have largely supplanted the human-driven liquidity providers, and some high-frequency technologies can now ill the role of the specialists and market makers of old. he result can be a substantial increase in the eiciency of US equity markets, in terms of spreads coming in and transaction costs falling. A distinguishing feature of high-frequency strategies is that they use a new type of information. Traditionally, informed traders in markets were those who had better information on asset fundamentals—at longer time horizons, fundamental information determines asset prices. In the very short horizon, information related to the trading process predominates. To put the speed of typical high-frequency traders into perspective, NASDAQ reported an average of more than 580,000 orders per second, which translates into approximately a two-millionths of a second for a round-trip order—speeds for high-frequency are even greater.52 High-frequency trading generally exploits information related to order lows and the structure of the book to predict where market prices are going both in a single market and across markets. High-frequency traders implement strategies using interconnected electronic networks designed to take advantage of predictable behaviors in order low. he strategies of high-frequency traders are diverse and are tailored to the market structure of the particular asset class. For example, a highfrequency algorithm to trade equities would implement a very diferent strategy from a high-frequency algorithm to trade futures on equity indexes. While cash equity markets are fragmented and decimalized, the markets for equity futures are not. Similarly, ixed-income trading

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algorithms must have special defensive features built in to protect the trader from the shocks arising from public information events such as Treasury auctions results or scheduled government data releases. Fixedincome futures are cointegrated, meaning that individual contracts are not independent of each other due to linkages with the term structure, varying maturities, and the like. Algorithmic strategies must take account of inherent tendency for prices to move congruently. High-frequency trading is based on a thorough understanding of the price formation and trading volumes, and the success of these strategies illustrates that prices are not random walks over very short time intervals of smaller than a millisecond. he question is whether the high-frequency trade translates into a new investment paradigm. In a 2000 speech thenChairman of the US Federal Reserve, Alan Greenspan noted that electronic inance represents an acceleration of the process that noted economist Joseph Schumpeter many years ago termed “creative destruction”—the continuous shit in which emerging technologies push out the old.53 he advances in technology pose some challenges for inancial institutions and markets and for policymakers. Some institutions inevitably will suffer erosion of their franchise values as competitors, new and old, prove more adept at tapping the potential gains from the new technology. More research is needed to fully understand the efects of HFT on liquidity and price discovery more generally, and on other participants, such as institutional investors in particular. Capital mobility and market liquidity Trading requires capital irrespective of the market structure in which trading occurs. Even seemingly “free” trades such as short-selling or collateralized borrowing require capital in the form of margin. For example, traders can use a repurchase agreement (repo), a form of collateralized borrowing as a source of short-term cash. In a typical repo the trader uses securities such as corporate or Treasury bonds as collateral for their borrowing. But the trader cannot borrow the entire price and needs to inance the margin or haircut, the diference between the security’s price and the collateral value, with their own capital. he total margin cannot exceed a participant’s capital.

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Trading by any market participant is therefore contingent on their capital, which comes in the form of either having suicient cash or having the ability to obtain credit at acceptable terms. A market participant’s access to capital depends on, among other things, the institutional features of the market in which they operate, their credit rating, and access to debt capital, but also on the regulatory capital requirements tailored to their particular industry. Hedge funds face little regulation, and their capital consists of their equity capital that is supplied by the investors. Hedge funds may have some amount of permanent equity capital, but investors can withdraw their equity capital ater a initial lock-up period. Commercial and investment banks’ capital consists of equity capital in addition to its long-term borrowing, including secured credit lines.54 Banks also raise money using short-term uncollateralized loans such as commercial paper and promissory notes, or in the case of commercial banks, demand deposits. he trading activity of banks is largely based on collateralized borrowing such as repo. he margin again needs to be funded using the bank’s own capital—the details of these arrangements for dealer banks are more complicated than those for the hedge fund.55 Commercial banks are regulated and subject to the Basel Accord, supervised by the Federal Reserve System of the US banks. Brokerspeculators in the United States, including banks acting as such, are subject to the SEC’s “net capital rule” (SEC Rule 15c3-1). his rule stipulates that, among other things, a broker must have a minimum “net capital,” which is deined as equity capital plus approved subordinated liabilities minus “securities haircuts” and operational charges. Market makers are in principle subject to the SEC’s net capital rule, but the rule has special exceptions for certain market makers, such as NYSE specialists. hese specialists’ main regulatory requirements are those imposed by the exchange on which they operate. Since a specialist has an obligation to make a market, complying with the funding constraint is especially crucial for them since it directly impacts their ability to supply liquidity for exchange trading. he capital requirements of market makers, broadly deined to include all market participants who provide liquidity, such as brokers,

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dealers, specialists on the NYSE, or dealers on NASDAQ, highlights several important aspects of the market-making activity. In his presidential address to the American Finance Association, esteemed Stanford professor Darrell Duie argued that “slow-moving capital” inhibits the immediacy with which market makers react to supply and demand shocks.56 Duie deined “slow-moving capital” as short-term impediments to capital and explained that capital mobility afects market liquidity and the observed prices of traded securities. Capital can be restricted temporarily, for example, if margins on repurchase agreements (collateralized borrowing) increase, or more permanently, for example, in response to changes in regulatory capital requirements. Consider, for example, what happens when arbitrageurs who specialize in certain assets sufer signiicant losses in capital. A common hedge fund strategy before the 2007/2008 inancial crisis was the credit default swap-corporate bond basis trade. A credit default swap (CDS) is similar to an insurance contract in that it provides the buyer of the CDS with protection against the default of a corporate bond issuer. he buyer of default protection receives a single payment from the seller in the event of a default of the reference entity, the corporation that issued the bond, to cover their default losses.57 In exchange, the seller receives a series of payments from the buyer for the duration of the CDS or until the default of the issuer, whichever comes irst. he payments from the buyer to the seller, the CDS rate, is an annual premium paid for the coverage of default losses should the issuer default before maturity of the CDS contract. Because the CDS relects the credit risk of a corporate issuer, it trades in tandem with the issuer’s bonds with similar ranking and maturity. he CDS-corporate bond basis is the diference in the premium payment on the CDS and the yield spread on the corporate bond. To the extent that the basis becomes materially negative, an arbitrageur buys CDS protection and contemporaneously buys the corporate bond.58 he arbitrageur pays the premium on the CDS and receives the yield on the corporate bond, which is greater than the payment on the CDS. In a frictionless market with no impediments to trade, the CDS-bond basis will be mean reverting due to the actions of these arbitrage traders. During

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400 200 0 –200 –400 –600 –800 Jan-05 May-05 Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12 Jan-13 May-13

Difference in CDS Spread and Corporate Bond Spread (basis points)

normal market conditions, the CDS-bond basis is essentially zero (institutional details such as counterparty risk can, however, cause the basis to be slightly diferent than zero). he arbitrageur executed a proitable converge trade. In reality, the arbitrageur requires capital for this trade, for example, using a repurchase agreement that requires an initial margin payment—this efectively reduces the proit to the arbitrageur. In the event that the inancing or the margin payment becomes prohibitively expensive, arbitrageurs will not execute such basis trades, causing the CDS-bond basis to diverge from zero for an extended period of time. his is exactly what transpired during the 2008 crisis period. he CDS-corporate bond basis was extremely negative across broad portfolios of investment-grade and high-yield bonds, as shown in Figure 3.2. For the typical arbitrageur, borrowing was just too expensive. According to J.P. Morgan, the initial margin on corporate bonds increased from 5 percent in June 2005 to 10 percent in June 2008. In October 2008, the margin increased to 20 percent to 25 percent, and inancing for many hedge funds was simply not available.59

High Grade All CDS-Bond Basis

High Yield All CDS-Bond Basis

Figure 3.2 The corporate bond credit default swap basis for high-yield and investment grade bonds.

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Market Liquidity Risk

he most plausible explanation was the shortage of capital held during the inancial crisis by dealer banks, which was exacerbated by the increase in haircuts on corporate bonds in the repurchase market. Dealers hold bufer inventories of securities they intermediate. In the event that their balance-sheet capacity is depleted, their ability to intermediate markets is reduced. A dealer that intermediates a market in CDS basis trades locks up a substantial amount of their balance sheet capacity, both to make markets in the underlying bond, which calls for inding and holding the underlying bonds, and to handle two credit default swap counterparties, one with the arbitrageur and one with the counterparty taking the opposite side of the trade.60 As dealers regained balance sheet capacity with improvements in market conditions and some capital raising, the CDS basis reverted to more normal levels in 2009, as illustrated in Figure 3.2. Other impediments to a market participant’s capital, such as regulatory changes, are more permanent. For example, consider the case of bond trading, which takes place in the OTC market dominated by a limited number of dealers. hese dealers are also the main shock absorbers when supply and demand imbalances arise in the bond market. Regulatory initiatives following the 2007–2008 inancial crisis increased the level of capital that market makers were required to hold against bonds. Regulators’ objectives with these regulation were to improve market makers’ resilience and stability, but instead it increased the capital cost of holding bonds, which made it too expensive for them to hold bonds on their balance sheets.61 As a result, market makers were less willing to trade bonds, and a number of dealers withdraw fully or partially from market-making and proprietary trading activities in ixed income. Dealer inventories in corporate bond markets have fallen by nearly 79 percent to around $59 million from a high of $286 billion in late 2007. During the same period, the issuance of US investmentgrade corporate bond markets increased by about 50 percent to more than $3 trillion as issuers took advantage of low rates and investors search for yields. he mismatch between low trading inventories and high demand and issuance is one factor that is restraining liquidity in corporate bonds.62 Market liquidity in ixed-income bonds has declined amid a ballooning size of new debt issuance. For example, US Treasury issuance increased

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71

from $4.6 trillion in 2006 to $11.9 trillion in 2013, and corporate issuance increased from $5.5 trillion to $9.8 trillion over the same period. But liquidity has declined, as can be seen in the decline in the trading ratio across ixed-income securities, shown in Table 3.1. he trading capacity of the market has not kept up with the substantial growth in the size of the market. he importance of the liability structure of dealer balance sheets is not new. We explore the link between a dealer’s balance sheet composition and market liquidity in chapter 5, and between balance sheet composition and regulatory policies in chapter 6. he importance of capital applies more broadly to all market participants, including investors and speculators. When capital is restricted, market participants become reluctant to take on positions, especially capital-intensive positions in high-margin securities that force them to scale down from some markets, leading to wider bid-ask spreads, or to withdraw completely, leading to a trading halt. In their seminal paper, Professors Markus Brunnermeier and Pedersen developed a framework for understanding the causal link between funding and market liquidity.63 Traders provide market liquidity, and their ability to do so depends on availability of funding. Conversely, traders’ funding, that is, their capital and margin requirements, depends on the assets’ market liquidity. In a typical collateralized borrowing transaction, such as repurchase funding (repo), the borrower must post margin to cover the risk that they may

Table 3.1

US fixed-income market size and trading ratio

Fixed Income Market

Amount Outstanding ($ Trillion)

Trading Ratio

2006

2013

2006

2013

US Treasuries

4.3

11.9

31.6

12.4

Corporates

5.5

9.8

0.8

0.5

Mortgages

8.4

8.7

8.9

6.5

Municipals

3.2

3.7

1.9

0.8

Agencies

2.6

2

7.1

0.8

Note: The trading ratio is the annual dollar volume divided by the total debt outstanding. Source: SIFMA, April 2014 and BlackRock Investment Institute.

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Market Liquidity Risk

not be able to pay for the securities they are buying or deliver the securities they are selling. However, margin requirements depend in part on the security’s market liquidity. Under certain conditions, margins are destabilizing, and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. We revisit the Brunnermeier and Pedersen framework in more detail in chapter 6 in the context of asset pricing. he relationship between a market maker’s access to capital and their ability to provide market liquidity also furthers our understanding of market liquidity, and in particular it motivates the commonality in market liquidity. Bid-ask spreads widen simultaneously on many security markets when funding to market makers is restricted, afecting liquidity in all securities. his insight may help explain why liquidity can be correlated across stocks and across bonds and stocks. Comovements in liquidity for stocks handled by the same specialist on the NYSE are related to this ability to provide liquidity. his suggests that an increase in the cost of capital or increased risk exposure for a specialist leads to a simultaneous drop in liquidity for the stocks assigned to that specialist.64 Another empirical study showed that the liquidity of corporate bonds drops if the lead underwriter faces funding problems.65 Consider the liquidity of bonds underwritten by Bear Stearns, before being taken over by J.P. Morgan in 2008. In November 2007, Bear Stearns wrote down $1.62 billion and booked a fourth-quarter loss, and in December 2007, there was a further write-down of $1.90 billion. During these months, the liquidity of bonds underwritten by Bear Stearns decreased, compared to bonds underwritten by others. On March 16, 2008, Bear Stearns was taken over by J.P. Morgan. he liquidity gap between bonds underwritten by Bear Stearns and by others returned to zero in June 2008 ater Bear Stearns shareholders approved J.P. Morgan’s buyout of the investment bank on May 29, 2008. Concluding thoughts Technology and new regulatory policies are revolutionizing the way markets operate forcing us to adjust to new realities of increased transaction

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speeds, new paradigms of trade execution, new mechanisms for disseminating trading information, changes in regulatory capital requirements, and moves toward the central clearing of some OTC products. However, the fundamental purpose of markets has not changed—inancial markets exist to provide liquidity and enable price formation. his chapter highlights some of the core structural drivers of market liquidity—these will not change, but they may be recast as we adjust to the new market realities.

4

Asset Pricing and Market Liquidity

Introduction Traditional asset pricing models are based on the notion that aggregate market risks rather than individual risks are priced in a market that has reached an equilibrium between supply and demand from participants. he assets prices under this paradigm generally agree with the fundamental value of the asset, and all you need for asset pricing is knowledge of the cash lows or payof and a speciication of the discount factor. his traditional economic paradigm, discussed in chapter 1, further assumes that markets are frictionless, or perfectly liquid, and that capital is freely available. Yet this traditional paradigm has limited ability to explain empirically observed market behaviour because it either dismisses the issue of market liquidity as a friction or accounts for market liquidity by adding a transaction cost to the fundamental value. Market liquidity is typically incorporated as an exogenous transaction cost—an aterthought to asset pricing. he simplicity of this view is appealing, and for the ignorant market participant it may be suicient. It acknowledges the diference between the transaction price and the fundamental value of an asset, and further attributes this diference to the costs of trading. Incidentally, adding trading costs violates the basic assumption of frictionless markets on which most classical asset pricing models are based. Simply adding transaction cost to the fundamental value does not fully capture the intricacies of market liquidity or adequately explain diferences between fundamental values and traded prices. To shed light on the

76

Market Liquidity Risk

limitations of this simplistic approach, we consider two examples of asset price anomalies that can be explained by market liquidity. In our irst example, we look at pricing anomalies in government bonds in which pricing is not contaminated by other factors such as default risk, which is prevalent in corporate bonds. his allows us to focus on the pricing diferences due to factors such as market liquidity. In a 2004 study, Professor Francis Longstaf observed that the average yield of Resolution Trust Corporation bonds was between ten and sixteen basis points higher than the average yield of comparable Treasury bonds and that these diferences varied signiicantly over time.1 Longstaf selected the bonds in his study to be comparable along other dimensions—in particular, the bonds had corresponding maturities and zero coupon payments. he tax treatment for Resolution Trust Corporation and Treasury bonds are similar and there is essentially no diference in the default risk of Treasury bonds and Resolution Trust Corporation bonds—the US government guarantees full payment of Resolution Trust Corporation bond coupons and fully collateralizes their principal with Treasury bonds. However, unlike Treasury bonds, Resolution Trust Corporation bonds are rather obscure securities, in limited supply and therefore less liquid than their widely traded Treasury bond cousins. his comparison illustrates that investors are willing to accept a lower return on the more liquid treasury bonds when compared to the less liquid Resolution Trust Corporation bonds. In our next example, we consider the Standard & Poor’s (S&P) 500 equity index, one of the most commonly followed equity indices and representative of the US stock market. It is well know that adding a stock to the S&P 500 equity index results in an “index pop” or “index efect,” while deleting a stock from the index cause it to sufer a temporary price decline.2 hese types of price anomalies can be explained as disguised efects of market liquidity: the S&P 500 irms are followed more widely than individual securities and there is generally more information available on such irms, that reduces information asymmetry between investors and consequently improves the market liquidity of these securities. A better understanding of asset pricing implications of market liquidity may expand the utility of stock indexes as inancial tools.

Asset Pricing and Market Liquidity

77

Neither ignorance nor simply relegating market liquidity to transaction cost could have fully explained either one of the price phenomena illustrated by these examples. What we need are new asset pricing models. In this chapter, we discuss several frameworks of asset pricing that show us how to incorporate a market liquidity premium into asset prices and returns. Each of these frameworks develops a model by considering certain aspects of market liquidity. In each framework, we focus on speciic features of the liquidity process that will also further our knowledge of market liquidity more generally. he irst framework illustrates the relationship between the bid-ask spread and asset prices and shows how this relationship is afected by investors’ holding period. he second framework assumes that liquidity can also be generated as an endogenous part of the trading process such as trading in the over-the-counter (OTC) market, in which an investor who wishes to transact, must search for a counterparty, all the while incurring opportunity or other costs until one is found. his search-andbargaining model, developed by esteemed Stanford Professor Darrel Duie, difers from the simple bid-ask spread in that liquidity is endogenous to the trading process in which prices and liquidity are jointly determined. he next framework relies on a combination of behavioral economics and inance theory to arrive at an asset pricing model with liquidity motivated by the failure of the no-arbitrage principle discussed in chapter 1. Classical inance theory is agnostic to behavioral aspects of trading, and therefore cannot be used to explain the failure of arbitrage in certain instances. hese frameworks quantify the efects of the level of market liquidity on asset prices.3 Transaction cost reduces the return to investors, but there is also a commonality in liquidity, which implies that liquidity risk is necessary to characterize a inancial asset fully. he liquidity-adjusted capital asset pricing model discussed in the section titled “A meanvariance framework for pricing liquidity risk” incorporates liquidity risk and advances our understanding of liquidity as a random timevarying endogenous cost.

78

Market Liquidity Risk

Exogenous (“add-on”) cost of trading The bid-ask spread his section illustrates the relationship between the level of liquidity and asset prices based on the insights and simple model developed by the pioneers of market liquidity research, Professors Yakov Amihud and Haim Mendelson.4 Trading costs are the direct and indirect costs associated with trading a security. Direct costs include brokerage fees, transaction taxes, and other trade-processing fees that are easy to measure. he trading cost we look at in this section is the bid-ask spread. he bid and ask prices quoted in a market set the prices at which customers can buy from or sell to market makers. In this section, we consider the bidask spread more broadly as a measure of market liquidity. he Amihud-Mendelson model shows an inverse relationship between transaction cost and return, whereby an increase in transaction cost causes a pari passu decrease in the return. An interesting outlow of this model is that the relationship between the market return and the transaction cost is not linear, but the gross return increases with bid-ask spreads at a decreasing rate or, put diferently, return is a concave function of the bidask spread.5 he relationship between stocks’ excess monthly return and the bid-ask spread for a given level of systematic market risk is shown in Figure 4.1. he concave relationship relects the theoretical insight ofered by the Amihud-Mendelson model, which is that in equilibrium, less liquid securities are allocated to investors with longer holding periods. hese investors’ longer holding periods mitigate the compensation that they would have required for higher transaction costs on less liquid securities. Empirical research by Professor George Constantinides showed that large transaction costs typically have small price efects, conirming the concave relationship between bid-ask spread and return found by Amihud and Mendelson.6 A model of bid-ask spread and return

Consider the following simple model of bid-ask spread and return. An investor buys a security that he plans to sell ater h periods. During

Asset Pricing and Market Liquidity

79

2.0% 1.8%

Excess Monthly Return

1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2%

0.

00 0. 001 00 0. 301 00 0. 601 00 0. 901 01 0. 201 01 0. 501 01 0. 801 02 0. 101 02 0. 401 02 0. 701 03 0. 001 03 0. 301 03 0. 601 03 0. 901 04 0. 201 04 0. 501 04 0. 801 05 10 1

0.0%

Bid-Ask Spread (%) Figure 4.1

The relationship between a stock’s excess return and the bid-ask spread.

Source: Y. Amihud, H. Mendelson and L.H. Pedersen, 2013, Market Liquidity: Asset Pricing, Risk and Crises, Cambridge University Press. Figure PI.1, page 5.

this time, the security does not pay dividends or interest. he market is illiquid, and the proportional bid-ask spread at date t is denoted by st (our intention here is for the bid-ask spread to refer more broadly to the transaction cost). he bid and ask prices at that time are given by s at = mt  1 + t   2

(4.1)

and s bt = mt  1 − t  .  2

(4.2)

We assume that the midquote is equal to the fundamental value of the security, mt = μt. Suppose that investors require a return of r per period on the security, given its risk characteristics. For example, for a riskless security like a Treasury bill, the required return is simply the risk-free rate. If the market were perfectly liquid (i.e., st = 0 at any time), then the expected return on the Treasury bill would be the risk-free rate. Assuming that the market is not perfectly liquid, the maximum price that the investor is willing to pay at date t for a security with a cash

80

Market Liquidity Risk

low at a future date (t+h), given by the standard discounted cash low model, is at =

bt + h

(1 + r )h

(4.3)

.

We substitute the formulations of the ask price and the bid price in equations (4.1) and (4.2), into (4.3) to arrive at the relationship expressed in equation (4.4): s s 1 µt  1 + t  = µt + h  1 − t + h  .   2 2  (1 + r )h

(4.4)

Next, we rewrite equation (4.4) to express the current value of the asset at its discounted future value, adjusted for current and future transaction costs:

µt = µt + h

1

(1 + r )h

 1 − st + h   2  .  st   1+   2 

(4.5)

he last term is a measure of the illiquidity since it decreases respectively in both the current and future transaction costs st and st+h. he fundamental value at date t is correlated with illiquidity: the greater the current estimate of the future spread, the higher the transaction costs for investors and the lower the value of the asset to them. his is consistent with our example, in which the more liquid Treasury bills traded at a premium over notes, notwithstanding their identical payofs at maturity. In a liquid market, the bid-ask spread would also not depend on the quantities being traded. Empirical results show that the spread quoted in markets is not generally an exact relection of transaction costs because certain transactions may be traded either inside or outside the quoted spread. An alternative measure is the efective spread deined as the difference between the execution price and the midpoint of the quoted bidask spread. he efective spread is deined as ste = d( pt − mt ),

(4.6)

Asset Pricing and Market Liquidity

81

where d is an indicator variable assuming the value of 1 if the transaction is buyer initiated, or –1 if the transaction is seller initiated. Notwithstanding its simplicity, a limitation of bid-ask spread measures is that it relects liquidity at a particular time. In asset pricing and risk management applications, it is important to account for the fact that liquidity is uncertain and varies over time.

The Roll measure of effective bid-ask spread he economist Richard Roll proposed a measure for inferring an efective bid-ask spread directly from transaction prices.7 he Roll measure takes the size of the bid-ask spreads as a given. he spread calculated using the Roll measure is akin to an efective spread (the efective spread is expressed in terms of the transaction prices as deined in equation [4.6]). Trades can be executed within the quoted bid-ask spread, which can make the efective spread a more accurate relection of execution costs. he Roll measure may be best suited to exchange trading with an order book. he motivation for including it here is that it lays the groundwork for the idea that there is a relationship between the covariance in the security’s return and market liquidity, which forms the basis of some frameworks we discuss in chapter 5. he Roll measure is based on the following insight from trading in a limit order book: in the event that an order hits the bid-ask prices at random, the transaction prices bounce back and forth between them, straddling the midquote. he transitory deviations around the midprice are called the bid-ask bounce. Intuitively, this bounce engenders negative serial correlation in transaction-to-transaction returns even when the underlying value (midquote) follows a random walk. To see this, suppose that a buy market order arrives at time t, followed by a sell market order at time t+1. he irst trade is at the ask price, the second at the bid price. hus, the return between dates t and t+1 is negative. One accordingly expects a positive return between t+1 and t+2, the time of the subsequent transaction. Either the next market order is again a sell order executed at the bid price (in which case the return from t+1 and t+2 is zero) or it is a

82

Market Liquidity Risk

buy market order executed at the ask price (in which case the return from t + 1 to t + 2 is positive). A similar argument shows that, ater a positive return, one would expect a negative return. Roll exploits this intuition to construct an estimator of the bid-ask spread based entirely on the serial covariance of returns. Suppose that a security’s fundamental value, captured by the midquote, follows a random walk: Vt = Vt −1 + ε t .

(4.7)

where εt is a zero mean white noise such that E(εt) = 0 for all t and E(εt εs) = 0 for all t ≠ s. his variable represents the change in the value of the security due to new information between time t − 1 and t. As E(εt) = 0, the expected fundamental return is zero. his is a reasonable assumption if the time interval is assumed to be small, for example, one day. Assume that the bid-ask spread S is constant over time. he observed transaction price can be expressed as S Pt = Vt + δ t , 2

(4.8)

where δt is a random indicator of whether the transaction at time t took place at the bid or the ask price, δt = 1 if the transaction is initiated by a buyer, or δt = −1 if the transaction is initiated by a seller. In particular, the ask price at which a market buy order is executed by the dealer and the bid price at which a market sell order is executed by the dealer are Ask t = Vt +

S 2

and S Bid t = Vt − . 2 he transaction-to-transaction price change is S ∆Pt = ( Pt − Pt −1 ) = Vt + ∆δ t + ε t . 2

(4.9)

Asset Pricing and Market Liquidity

83

Roll made the following assumptions on the order arrival process: (a) Order low is balanced: market orders are equally likely to be a buy 1 or a sell order. In other words, P (δ t = 1) = P (δ t = −1) = , or equiva2 lently, E(δt) = 0 for all t. (b) here are no autocorrelations in orders. Buy and sell market orders are serially uncorrelated, in other words, E(δt δs) = 0 for all t ≠ s. (c) here is no efect on the midquote. Market orders are assumed to carry no news, meaning that they are uncorrelated with current and future innovations in fundamentals: E (δ t ε t ) = E (δ t ε t +1 ) = 0 for all t. (d) here is constant (zero) expected return. he fundamental value follows a random walk so that E ( Pt − Pt −1 ) = E ( ε t ) = 0 for all t. Under this set of assumptions, Roll’s measure follows: E ( Pt − Pt −1 ) = 0

(

)

cov ∆Pt , ∆Pt −1 = −

S2 . 4

(4.10)

Roll’s measure captures a useful measure of transaction cost and it utilizes readily available transaction prices as opposed to requiring data on bid-ask spreads. he Roll measure is useful for longer-horizon empirical studies since it rests on the assumption that the fundamental value or midprice following a random walk and market bid and ask transactions are balanced. Professor Hans Stoll, in his 1999 Presidential address to the American Finance Association, used the Roll measure to compare the magnitude of trading costs between stocks traded on the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ). Stoll studied 1,706 NYSE stocks and 2,184 NASDAQ stocks in the three months ending on February 28, 1998. He estimated Roll measures of 3.81 cents on the NYSE and 11.15 cents on NASDAQ ater controlling for diference in market capitalization between these exchanges.8 he Roll measure was also within the quoted spreads of 7.9 cents on the NYSE and 12.6 cents on NASDAQ. Stoll’s study of liquidity diferences between stocks on the NYSE and NASDAQ further highlights the importance of market structure, as we discussed in chapter 3.

84

Market Liquidity Risk

Market impact measures of Amihud and Kyle he market impact measures build on the idea that a given volume of securities in a liquid market can be traded without signiicantly afecting their prices. Market impact measures were spearheaded by respected professor and fellow of the American Society of Finance Albert Kyle based on insights from market micro-structure.9 Kyle proposed that market makers cannot distinguish between order low generated by informed traders and order low that may indicate uninformed or noise trading. Market makers set prices that are increasing functions of the imbalance in the order low, which creates a positive relationship between transaction volume and price change. he price impact of a particular trade will therefore be smaller in a liquid market. Kyle showed that market “depth,” a characteristic of a liquid market, discussed in chapter 1, is indeed a result of the optimizing behavior of market makers. Kyle’s liquidity measure is the order low necessary to induce prices to rise or fall by one dollar. he measure is implemented using intradaily transaction data that may not always be available. A related market impact measure that can be implemented using readily available daily transaction prices is the popular Amihud measure, proposed by Professor Yakov Amihud. he Amihud measure uses the intuition that a security is less liquid if a given trading volume results in a greater move in its price.10 he Amihud measure utilizes data on traded security prices and the average trading volume over a given time period deined as  Absolute return  . (4.11) Amihud measure = Average of the per period   Traded volume  he traded volume for stocks is the product of the number of shares traded on a given day and the closing price at the end of the day. he Amihud measure can be interpreted as the price response associated with one dollar of trading volume—serving as a rough measure of the price impact. he popularity of this measure lies in the ease with which it can be implemented using accessible transaction prices and the fact that it can be implemented with time series data to capture changes in market liquidity over time.

Asset Pricing and Market Liquidity

85

Liquidity premium of a search-and-bargaining model As discussed in more detail in chapter 3, one of several distinguishing features of the OTC market compared to [a] typical exchange-traded market, is the presence of a market maker or dealer who intermediates trade. Price formation in the OTC market occurs through a bargaining process between market makers and their customers. Trading in the OTC market is typically slower than on exchanges, because searching for counterparties who can ensure optimal execution takes time. An everyday example of this type of search-and-bargaining market is the residential real estate market, in which home prices are inluenced by the process of an imperfect search, such as impatience on the part of sellers and the outside options of buyers and sellers, and imperfect search factors, such as weather, the school calendar, and other personal changes. In the standard treatment of dealer markets, dealers hold an inventory of securities, and manage liquidity, that is, transaction cost, to control their inventory.11 Another standard treatment assumes that market makers are exposed to adverse selection by “informed” traders, such as irm insiders. Transaction cost represents the market maker’s compensation against adverse selection due to information asymmetry.12 Both these views, inventory management and scenarios of asymmetric information, deine market liquidity as compensation for a risk faced by the market maker. he compensation of the market makers comes in the form of the bid-ask spread, an exogenous addition to transaction prices. Professors Darrell Duie, Nicolae Gârleanu, and Lasse Pedersen expanded our thinking about market liquidity by shiting the focus from the market maker to the behavior of a typical customer or buy-side investors in the OTC market.13 Shiting the focus to the customer relects the reality of OTC markets, in which bid and ask prices are partly determined by investors’ outside options. For example, consider a multibillion-dollar investment manager and a portfolio manager at regional bank in the US Midwest who both want to execute an interest rate swap to hedge their portfolio. he larger portfolio manager has more counterparties to choose from than the typical regional bank manager, and he can therefore “shop around” for the optimal trade price, to ensure lower execution costs relative to smaller investors. he typical inventory model does not take

86

Market Liquidity Risk

into account this type of diferential treatment across diferent investors, which clearly does not relect the reality of this market. Customers are heterogeneous agents, and many factors such as the frequency and the size of their trading, and their credit ratings afect their accessibility to other market makers and their ability to secure counterparties. In their setup, Duie, Gârleanu and Pedersen negate the need for dealers to hold inventory by assuming that market makers access the interdealer market to augment their trading. hey show that in a market with heterogeneous investors, sophisticated investors with better access to market makers receive tighter bid-ask spreads because of improved outside options as compared to other investors. According to this view, the bid-ask spread has a cross-sectional component that could be diferent for the same securities transacted by diferent investors. Inventory-based models do not imply such a diferential treatment across investors. his theory adds another tool to the kit and highlights a diferent aspect of market liquidity that is consistent with behavior in certain OTC markets, such as interest rate swaps and foreign exchange, in which asymmetric information is limited. Investors rarely have material private information about the current level of interest rates, so standard information based explanations of bid-ask spreads are not compelling in these markets. he Duie, Gârleanu, and Pedersen framework builds on the dynamic interaction between dealers and their customers in a dynamic search-andbargaining setup. he search-and-bargaining model expands our toolset of liquidity models in two ways: it adds search costs as an additional consideration of market liquidity, and it relates the liquidity premium to the dealer’s bargaining power and search costs, essentially assimilating liquidity as an endogenous component of the trading process. Setup of the search-and-bargaining model In this section, we analytically derive an asset pricing model in a search-andbargaining market. he search-and-bargaining model developed below is an asset-pricing variant roughly based on the famous Diamond coconut model. he coconut model was devised by Peter Diamond, the 2010 Nobel laureate to understand behavior in labor markets.14 he Diamond model

Asset Pricing and Market Liquidity

87

also captures the essence of a search-based economy by envisioning an island populated by individuals who can only consume coconuts. Agents in this model are always in one of two states: they are both carrying a coconut and looking for someone with whom to trade it, or they are searching for a palm tree in order to possibly pick a coconut. We can contract a hypothetical market where trading between agents mirrors Diamond’s island economy. his market has two kinds of agents: investors and market makers. We assume all agents to be risk neutral and ininitely lived. Agents engage in the repeated trade of a single nonstorable consumption good.15 Market makers hold no inventory and maximize proits. Market makers have access to an interdealer market on which they unload their positions. Short sales are not allowed. Investors can invest in two types of securities: a riskless asset paying one dollar per period forever, referred to as a consol bond,16 and a riskfree bank account paying an interest rate of r. he consol bond can be traded only when an investor inds a market maker according to a random search model described in more detail below. he bank account represents a liquid security that can be traded instantly. In any given period, investors can choose not to hold the asset or can hold at most one unit of the asset. We assume a fraction q of investors is initially endowed with the asset. Since investors can hold at most one unit of the security, this would be equivalent to saying that there is a inite supply, q, of the consol bond. he investors are not all the same: some investors, referred to as “high rollers,” bear no cost for holding the asset. Other investors, referred to as “low rollers,” incur a holding cost of c per period, so their cash lows from holding the asset are reduced by the holding cost, $(1-c) per period. he holding cost of low rollers may relect constraints, such as a less favorable tax rate than that faced by high-roller investors, higher inancing cost, and a lower personal use of the asset or the immediate need for cash. Assume that the low roller’s intrinsic liquidity state is “low.” At the end of each period, investors switch from being low rollers to being high rollers and vice versa, with a probability ψ. To summarize, there are four types of investors, classiied according to o their ownership of the asset and by their per-period holding cost: (i) π h : no high rollers who own the security; (ii) π h : high rollers who do not own no the security; (iii) π lo : low rollers who own the security; and (iv) π l : low

88

Market Liquidity Risk Table 4.1 Investor types in the search-and-bargaining model Owner

Nonowner

High roller

π ho

π hno

Low roller

π lo

π lno

rollers who do not own the security. he full set of investor types shown in Table 4.1 is I = {π ho , π hno , π lo , π lno } , where the subscript “l” and “h” indicates the investor’s liquidity state, low or high. he superscript “o” and “no” distinguishes respectively between owners and nonowners. he proit of high rollers is maximized when they own the asset; therefore, high-roller nonowners want to buy the asset. he proit of low rollers is maximized when they do not own the asset; therefore, low-roller owners want to sell. When two agents meet, they will trade, only if doing so is mutually beneicial. Buyers and sellers need to search for a market maker in order to execute their orders. When an investor meets a market maker, they bargain over the terms of the trade. Investors sell to market makers at the bid price, b, and investors buy from market makers at the ask price a. We assume that the probability that the investor inds a market maker in any given period is φ , so that on average the investor will have to search for 1/ φ periods before execution of his order. his delay generates a search cost for low rollers, who bear the holding cost, c, in each period so that their expected search cost is c / φ. he equilibrium price depends on agents’ outside options, which will be relected, among the other variables, in the bid and ask prices, b and a. he price paid by investors for the security is as follows: 1− z   1  2ψ  1−φ a= − S ,  2   r  r (1 + z )

(4.12)

where S is the market maker’s bid-ask spread and z is an index of the market maker’s market power. We can also think of this index as representing an investor’s access to multiple, competitive market makers. he ask price in equation (4.12) is the present value of the payments from the asset, 1/r reduced by a liquidity premium.17

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Consider a special case of the model, where we assume investors do not expect their valuation to change over time, that is, ψ = 0, and the liquidity premium vanishes. he ask price reduces to the fundamental value of 1/r. High-roller nonowners expect to earn $1 forever once they buy, so they are willing to pay at most the discounted value of payment the $1 (their holding period being ininite). Next, consider trading in a market with search-and-bargaining frictions, modeled as ψ > 0. he second term in equation (4.12) does not vanish, and the trade price of the asset is less than the fundamental value due to the liquidity premium. he liquidity premium is proportional to the bid-ask spread S charged by market makers and increases in the bargaining power of the market maker. A value of z = 1 represents a monopolistic market maker, and smaller values are accordingly associated with less maker power and a lower cost of trading to investors, assuming everything else stays the same. he liquidity premium decreases if it is easier to meet a market maker. he investor sustains his holding cost for a shorter time, and his expected search cost c / φ is small. We mathematically summarize the narrative explanation of trading in the hypothetical OTC market discussed in the preceding paragraphs in equation (4.13). he bid-ask spread in the latter market is expressed as S = a −b =

(1 + z ) c . 2 (r + 2ψ ) + (1 − 2ψ ) φ (1 − z )

(4.13)

he expression for the bid-ask spread shown in equation (4.13) is derived in the Appendix. his model predicts that the spread is increasing in the holding costs c and in dealers’ bargaining power z: when their holding costs are high, sellers’ valuation of the asset is smaller, and if dealers have substantial bargaining power, sellers will be forced to accept a low price. Hence, on both accounts the bid price quoted by dealers will be low. he efect on the spread of the probability φ of meeting a dealer is ambiguous. Numerical example We illustrate some of the search efects on asset pricing and market making with a numerical example. Figure 4.2 shows the market maker’s

90

Market Liquidity Risk 20.00

19.80

19.60

Price

19.40

19.20

19.00

18.80

18.60

18.40 1

10

100

1,000

10,000

Intensity of an Investor Meeting a Market Maker Price Figure 4.2

Bid Price

Ask Price

The estimated mid, bid, and ask prices as a function of finding a market maker.

Source: D. Duffie, N. Gârleanu and L. H. Pederson, November 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, 1815-1847. Figure 1.

bid and ask prices as a function of the probability of inding a market maker. Assume that market makers have some bargaining power, z = 0.8, and that interest rate is r = 5%. In the extreme case of a monopolistic market maker, represented by z = 1 in equation (4.13), the bid-ask spread is given by, S = a −b =

c . (r + 2ψ )

In the case of competitive market makers, shown in Figure 4.2, the bidask spread reduces to zero, and the price approaches its fundamental value of 1/r = 20 if the search becomes more eicient. In contrast, with a monopolistic market maker, the bid-ask spread increases with search eiciency, and the price is bound away from its fundamental value. his result is intuitive. If inding market makers becomes more eicient, more trades are executed. If market makers have all the bargaining

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power, the bid-ask spread is higher than if market makers do not have all the bargaining power. he model also shows that investors with better outside options receive tighter bid-ask spreads. The theory of limited arbitrage Empirical results show that the process of arbitrage sometimes fails to bring prices close to the fundamental values implied by standard models. An example of such can be found in the market for US Treasury bonds. he Treasury issues bonds during regularly scheduled auctions, and these newly issued bonds are referred to as on-the-run US Treasury bonds. At any given time, of-the-run Treasury bonds issued at previous auctions can still be outstanding. It is possible to ind an on-the run and an of-the run bond with an equal amount of time to maturity and comparable cash lows. However, the on-the-run Treasury bond typically trades at a higher price (or equivalently a lower yield) than a comparable of-the-run Treasury bond. his suggests the following trading strategy. Sell the on-the-run bond short and use the proceeds to purchase an of-the-run issue with a comparable maturity date. Hold these trades until the next auction date or until maturity to lock in the yield diferential. his strategy involves two securities with similar risk characteristics that trade at diferent prices, which is a clear violation of the law of one price. If this were truly a theoretical arbitrage opportunity, why would investors leave this money on the table? A critical diference between the theory and the execution of the strategy in the inancial markets is the need for money or capital. In the example of the Treasury bond strategy, investors would typically buy or sell the bonds in the spot market and execute a repurchase (repo) or a reverse repurchase transaction to inance the trade. However, transacting in the repo market is not “free” because of the need for margins or haircuts, which are usually posted upfront. If you do not have enough wealth for the margin payment, you can employ leverage that will essentially enable you to execute the strategy using borrowed money. Leverage will expose the investor to additional risks, for example timing risk, when the loan needs to be repaid before the mispricing converges, or market risk, when the mispricing diverges. Timing risk requires a premature liquidation of

92

Market Liquidity Risk

the position. Market risk requires posting of additional collateral which could lead to the risk of increased borrowing during adverse markets. A classic example is the demise of the hedge fund Long Term Capital Management (LTCM). LTCM implemented trades that relied on no arbitrage mispricing which they funded using borrowed monies. he ratio of the debt-to-equity in the beginning of 1998 was approximately 25 to 1. In the wake of Southeast Asian inancial collapse and trouble in Russia in the spring of 1998, LTCM’s arbitrage strategies started losing money requiring them to post additional collateral and us capital to fund margin calls. By December 1998, the portfolio was unwound and the fund was liquidated. Several more recent examples of asset mispricing due to failure of arbitrage relationships during crisis periods were discussed in chapter 2. Arbitrage relationships may also break down due to factors prevailing during normal markets. Consider, for example, the removal of a stock from the S&P 500 equity index, discussed in the introduction to chapter 4. Upon the decision by the S&P to remove a particular equity from the S&P 500, the index funds replicating this index, have a high incentive to sell the deleted equity on the efective date. A very small set of investors are actively considering to purchase the deleted equities near the efective date. hey require substantial price discounts. It is also likely that many of the investors planned to sell their positions over time to investors who, on the efective date, were not immediately available or aware of the opportunity to buy. Anticipation of the price at the efective date appears to have led to a substantial reduction in price between the announcement date and the efective date.18 Another aspect that distinguishes real-world arbitrage from theoretical arbitrage is the fact that there is a separation between investors who supply capital and investors who implement arbitrage strategies. Pension funds, endowments, and wealthy retail investors with either limited knowledge or limited interest in inancial markets typically invest their money with institutional investors such as hedge funds and proprietary trading desks that have the specialized knowledge and skills needed to execute an arbitrage trading strategy. his creates a responsiveness of funds under management to past returns. Using their own money, arbitrageurs typically allocate funds based on expected return. In the event that mispricing increases and

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the return decreases, an outside investor may only observe the arbitrageurs losing their money, and without detailed knowledge of the trade, they may withdraw their capital to invest in more favorable strategies or with a different investment manager. However, when mispricing increases, which incidentally implies an even higher expected return, the arbitrageurs may have to liquidate their position to return borrowed monies. In such cases, arbitrageurs can become capital constrained exactly when the best opportunities are available. his dynamic could signiicantly limit arbitrageurs’ efectiveness in achieving market eiciency because they do not have the capital resources to eliminate mispricing. his is particularly problematic during times of crisis, when their actions are most needed, which could lead to mispricing persisting for a longer period of time. Inspired by these dynamics of real-world arbitrage processes, Professors Andrei Shleifer and Robert Vishny19 developed an asset pricing model. he Shleifer-Vishny model augments the classical asset pricing considerations of risk and return with behavioral inance aspects of investment and capital allocation to quantify mispricing due to the failure of arbitrage. he model belongs to the class of “agency” models—a fusion of classic asset pricing and behavioral inance. The Shleifer-Vishny agency model he Shleifer-Vishny agency model considers two types of ictitious agents: third-party investors and arbitrageurs. he arbitrageurs are professional, highly specialized investors, such as large fund managers. hird-party investors are wealthy individuals, endowments, or other investors with limited knowledge of the speciic markets. hese investors do not trade on their own, but rather invest money with arbitrageurs. Assume that trading is limited to a generic security such as a zero-coupon bond, and that trading can occur in one of three periods. To capture the funding constraints faced by real-world arbitrageurs, we assume that the arbitrageurs can only be active in either the irst or the second period, but not in both, and that agents cannot take a position of more than one unit in either security.20 Assume that at date 0 there are two similar zero-coupon bonds, bond A and bond B. Both bonds are correctly priced at date 3. he price of each

94

Market Liquidity Risk

therefore equal the fundamental value V. he risk-free interest rate is set to zero, so that in the event of no arbitrage, the price of the two bonds should be equal at each of the three dates. At date 0, there is an exogenous shock that causes the price of bond A to fall below that of bond B by an amount M0 > 0 such that P0A = P0B−M0. Bond B is priced correctly at P0B = V. Bond A is undervalued, and the size of the mispricing is M0. his price diference presents the following arbitrage opportunity: purchase the cheaper bond B for P0B and sell bond A for P0A collecting a cash low of M0. here is a risk that the mispricing can increase during the next period, in which case the arbitrageurs would have been better of to wait before implementing the strategy. Assume that the mispricing increases to M1 > M0 with probability κ or disappears with probability (1−κ). At date 2, the assets pay of, so any mispricing is eliminated: P2A = P2B = V. Before analyzing the prices in this model, consider the theoretical extremes of the model. If we assume arbitrageurs have unlimited resources and trade in a frictionless, eicient market, they will immediately counteract the mispricing and keep prices in line with their fundamental values. An alternative scenario is one in which arbitrageurs have limited capital, but free access to markets. Even if the mispricing increases, they can replenish lost funds by raising more money. In subsequent sections we describe additional versions of the framework, in which investors are forced to prematurely liquidate arbitrage strategies and in which arbitrageurs have limited access to additional funding.

Limited arbitrage due to risk of early liquidation In this version of the model, we assume that the arbitrageur is a fund manager and that their allocated capital depends on their past performance.21 Such arrangements are typical in the money management industry, in which hedge fund, pension fund, or mutual fund managers invest either their own money or money from other wealthy individuals. he arbitrageurs have a choice whether to execute the arbitrage strategy at date 0 or at date 1.

Asset Pricing and Market Liquidity

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Under the irst scenario, the arbitrageur chooses not to intervene at date 0, and to intervene at date 1 only if mispricing persists. By deinition, he has zero cash lows at date 0. At date 1, he has the opportunity to set up an arbitrage portfolio only of the mispricing of bond A increases; therefore, he gets a cash low M1 with probability κ. If instead the mispricing disappears, the arbitrageur can no longer intervene proitably, and will get no cash low. he arbitrageur’s expected proit if he waits to intervene at date 1 is Π wait = κ M1 . Suppose the arbitrageur chooses not to wait and decides to intervene at date 0. He then pockets the mispricing M0. With probability (1−κ), the mispricing disappears at date 1, and he chooses to close out his position. With probability κ, the mispricing increases, so that the market value of the portfolio declines from 0 to M0−M1 < 0. In the second scenario, the arbitrageur does not liquidate his position at date 2, so there is no additional cash low at date 2, and he closes the position at date 3. he arbitrageur manages the investors’ capital, which is sensitive to his performance. In this scenario, we are essentially assuming that investors will not remove their capital. Under the third scenario, the arbitrageur executes the strategy at date 0, when the mispricing persists at date 1. he third-party investors choose to remove their capital from the fund because they cannot tell whether the increase in mispricing is transient or relects a true loss in value of the asset. he arbitrageur is therefore forced to liquidate his position at a loss, because he sells bond A at P1A = V − M1 and covers his short position in bond B by buying bond B at a price V. We assign a probability ϕ to the likelihood of scenario three. herefore, intervention at date 0 yields an expected proit equal of Π

do not wait

(ϕ ) = M 0 − κφ M1 .

he arbitrageur’s choice hinges on the following trade-of. Under the irst scenario, in which the arbitrageur chooses to intervene at date 1, he forgoes the gain M0 from exploiting the mispricing wait at date 0. herefore, if Π ≥ M 0 , all arbitrageurs prefer to defer their intervention.

96

Market Liquidity Risk

Under the third scenario, the arbitrageur intervenes at date 0. Suppose the mispricing is expected to decrease κM1 < M0. In this case, the optimal strategy depends on the sensitivity of investor capital to interim performance. Waiting is optimal if an only if Π wait > Π do not wait or

κ M1 > M0 − κϕ M1 . We can rewrite this equation to show that waiting is preferable if the risk of forced liquidation is high enough. Where “high enough” is deined as the probability of forced liquidity ϕ being greater than ϕ ,

ϕ =

M0 − κ M1 . κ M1

(4.14)

Suppose bond A is even cheaper at date 1. In other words, we expect the mispricing to decline κ M1 < = M 0 . Even in this scenario, the arbitrageur may prefer to postpone intervention to date 1 if sensitivity to ϕ of the risk of early liquidation is great enough. If this risk is low instead (0 < ϕ < ϕ) he intervenes at date 0. he relationship also shows that the arbitrageur’s choice will depend on the proits M1 from delayed intervention. Even if the liquidation risk ϕ is low, investors may prefer to postpone intervention if the proits from waiting are large enough. A key insight from this model is that the arbitrage process can be inefective in bringing prices back to fundamental values under certain circumstances. For example, the model showed that, when mispricing increases and an arbitrageur exhibits losses, capital constraints may inhibit him from executing market stabilizing strategies at a time when it is most needed in the market. hese results are very closely related to studies of market liquidity. When assets trade well below their fundamental values but there are no buyers of the asset, asset prices relect a negative liquidity premium, or an illiquidity discount. he natural buyers of the assets may be prevented from buying cheap assets because of capital constraints, so the market illiquidity persists.22

Asset Pricing and Market Liquidity

97

Liquidity premium according to the limits to arbitrage theory To derive the liquidity premium, we extend our basic model to include nonspecialists, who know that they have less expertise than arbitrageurs. We assume that the nonspecialists are overly pessimistic as they value the bond at V − δ even though it will pay V for sure at date 2. We assume that δ > 0 and larger δ denotes the larger price discount expected by the nonspecialists. Suppose that the mispricing M1 that occurs in the market for bond A at date 1 is due to a shock to the nonspecialists. When this shock occurs, the nonspecialists sell their holdings of bond A. We assume the aggregate sale from nonspecialists at date 1 to be 1 1 Supply from Non − Specialists = 1 + (PA1 − V ) = 1 − M1 . δ δ We assume that the sell orders from the nonspecialists cause the mispricing of bond A and that it can only be corrected if purchases by arbitrageurs ofset the supply. However, the arbitrageurs’ ability to do this is limited since they must allocate capital between diferent strategies (this is represented by intervention at date 0 and at date 1 in the model). Suppose that mispricing at date 0 is very pronounced. In this case, arbitrage capital will be massively invested to harness the date 0 mispricing, leaving little available to bet against the mispricing at date 1. Mispricing at date 1 will tend to persist and increase in the mispricing at date 0. he probability φ(i) with which an arbitrageur would be forced to liquidate his position prematurely in the event that the mispricing increases at date 1 was deined in the section titled “Limited arbitrage due to risk of early liquidation.”23 To determine the equilibrium price of bond A at date 1 when a supply shock occurs, we resort to the basic economic principle that the equilibrium price occurs at the intersection of supply and the demand. he supply from the nonspecialists and arbitrageurs who are forced to liquidate their position prematurely must be absorbed by those arbitrageurs who still have capital to invest at date 1 because they did not intervene at

98

Market Liquidity Risk

date 0. Hence, the equilibrium price of asset A at date t = 1 is given by the condition that the sum of the nonspecialists’ and arbitrageurs’ sell orders should equal the arbitrageurs’ buy orders. he formula for the market clearing condition under this model is 1 1 1 2 1 − V + PA1 + ϕ = 1 − ϕ. δ δ 2

(4.15)

his yields the equilibrium price of bond A at date t = 1: 1 2 pA1 = V − δ  ϕ + ϕ . 2 

(4.16)

We recognize the general form of this equation: market price equals the fundamental value V adjusted by a liquidity premium. he liquidity pre1 2 mium in this model is deined as [−δ  ϕ + ϕ ]. he liquidity premium 2  increases in the mispricing expectations of the nonspecialists and by the sensitivity of capital under management. Recall that the indiference condition ϕ deined in equation (4.13) provides the relationship between the expected level of mispricing at date t=1 and the fraction of the arbitrageurs who intervene at date t=0. he fraction who intervene at date 1 determines the mispricing or liquidity premium in equation (4.15): 1 M1 = δ  ϕ 2 + ϕ . 2 

(4.17)

In equilibrium, the market-clearing condition is given by the faction ϕ of arbitrageurs intervening at date 0 and the date t=1 mispricing M*1 that simultaneously solves equation (4.13) and equation (4.16). he equilibrium mispricing is shown graphically in Figure 4.3. *

Application of the agency model to a funding crisis he market-clearing condition derived in equation (4.14.) implied that a suicient number of arbitrageurs exist who are able execute buy orders to ofset the supply of the assets. In this section, we consider a special case of this model, in which the funding of these agents is exhausted so that there

Asset Pricing and Market Liquidity

99

M1 Indifference curve defined in equation (4.13)

Indifference curve defined in equation (4.16)

M1* M0 2k ϕˆ* Figure 4.3

ϕˆ

Mispricing and allocation of arbitrage capital.

is no demand for bond A. his will lead to a depletion of market liquidity for the asset when it is critical, which will in turn lead to a very sharp fall in asset prices. To capture this point in the model of the previous section, suppose that with very low probability, the arbitrageurs who saved their capital for intervention at date 1 are hit by an unexpected cut in inancing at that date, possibly due to a credit crunch or to a capital loss in unrelated business. We call this a “crisis” state. Further assume that the crisis was unexpected. It does not afect the choice of a strategy at date 0, so that ϕ is determined by equation (4.13) as before. Hence, if the economy is not in crisis, the equilibrium mispricing is M1*, as explained in the section titled “Liquidity premium according to the limits to arbitrage theory” and as shown graphically in Figure 4.3. If a crisis does occur, however, the clearing condition deined in equation (4.14) is altered since there are no buyers to absorb the sell orders placed by other market participants. Formally, the new clearing condition is: Supply from Non-Specialists + Supply from Liquidating Arbitrageurs = 0 1 1 1 2 1 − V + PA1 + ϕ = 0, δ δ 2 so the magnitude of the liquidity premium in crisis state is 1 M1crisis = δ  ϕ 2 + 1 . 2 

(4.18)

100

Market Liquidity Risk

Compared to its equilibrium value in normal times, the crisis state mispricing of the asset increases by * M1crisis − M1* = δ (1 − ϕ ).

(4.19)

his implies that the illiquidity discount is higher and the price of the asset is lower during the funding crisis at date 1. he mispricing during the crisis will be more dramatic for larger values of δ. he reason is that if the funding crisis prohibits the natural buyers, in this case arbitrageurs, from buying the asset, nonspecialists are the only ones let to hold the asset and in equilibrium must become net buyers. he nonspecialists’ valuation of the asset is V − δ. Hence, higher δ means that nonspecialists value the assets less, which would most likely occur during times of distress in the market. hat is, the disappearance of funding capital at date 1 leads to a sharper fall in prices when δ becomes greater. his is illustrated in table 4.2, which compares the mispricing at date 1 in normal and crisis times. We can see that the crisis is associated with a greater liquidity premium, but that its magnitude is much greater if δ is higher, or, put differently, the liquidity premium increases in a lower reservation price of the nonspecialists. When δ is larger, the drying-up of the liquidity normally supplied by arbitrageurs has a more dramatic efect. In fact, the arbitrageurs who are forced to close their positions at date 1 help push the price even below the valuation of the nonspecialists, who in the crisis end up absorbing sell orders at suiciently discounted prices of the security. he size of the mispricing M1 relects the price impact of market illiquidity. he limits to arbitrage argument supports episodes of contagion across asset markets. In the event that the capital of arbitrageurs may Table 4.2 Liquidity premium with and without a funding crisis M1*

M1crisis

δ = 0.9

1.29

1.55

0.26

δ = 0.1

2.8

10.27

7.47

M1crisis - M1*

Asset Pricing and Market Liquidity

101

be insuicient, since they draw from the same pool of capital to absorb shocks in diferent markets, a drop in their capital may force them to liquidate positions in other markets, afecting asset prices in multiple markets. We consider the mechanisms whereby the shock in one market afects other markets in more detail in chapter 6. A mean-variance framework for pricing liquidity risk he classical mean-variance framework assumes a perfectly liquid market, as explained in chapter 1. Market liquidity, however, is pivotal in real-world markets, and investors require compensation for bearing systematic liquidity risk over and above the compensation for market risk. Market liquidity is also uncertain and varies over time. In this section, we discuss an expansion of the classic mean-variance framework developed by Professors Viral Acharya and Lasse Pedersen that includes market liquidity risk.24 he liquidity-adjusted capital asset pricing model (LCAPM) provides us with the tools needed to explain how liquidity risk and commonality in liquidity afect asset prices. In particular, a security that has high average illiquidity also tends to have high commonality in liquidity, high return sensitivity to market liquidity and high liquidity sensitivity to market returns. he traditional capital asset pricing model (CAPM) assumes that inancial markets are frictionless and that trades are executed at no cost. he Acharya-Pedersen pricing model incorporates market liquidity as stochastic trading costs. Stochastic trading costs are representative of real-world markets in which market liquidity is uncertain and varies over time. he other assumptions of the traditional CAPM—risk adverse investors maximizing their expected utility under a wealth constraint—are kept intact, so LCAPM reduces to the traditional CAPM when trading costs are zero. LCAPM complements the other theoretical pricing models with constant trading costs, for example, the search and bargaining model discussed in the section titled “Liquidity premium of a search-and-bargaining model,” or the simple bid-ask spread model with exogenous trading costs discussed in the section titled “Exogenous (‘add-on’) cost of trading.”

102

Market Liquidity Risk

Liquidity-adjusted capital asset pricing model he key insight of the traditional CAPM is that investors earn a higher expected return for greater systematic market risk that cannot be diversiied by any single investor.25 More formally, an investor earns a net return from a position in a security j: E (r j ) = r f + β j  E (r M ) − r f  .

(4.20)

he correlation coeicient or beta βj is deined as

βj =

cov (r j , r M ) var (r M )

. (4.21)

he beta tells us how the return on the security and the return on the market move together. In this model, we assume that agents can borrow and lend at the risk-free interest rate rf, which is determined exogenously. Real-world inancial markets sufer illiquidity. Furthermore, liquidity is uncertain and it varies over time. To capture the nature of liquidity, we can model it as a stochastic process. Assume that agents can buy at the i i i market-clearing price pt , but must sell at pt − ct . In this model, liquidity is a random variable, cti, which represents the per-security cost of transacting. he aggregate market measure of illiquidity is cM. Short selling is not allowed. To derive the liquidity-adjusted version of the CAPM, we express the net return in terms of the gross return Rj and a transaction cost cj: r j = Rj − c j. he liquidity-adjusted CAPM expressed in terms of the gross return and transaction cost is as follows: E ( R j − c j ) = r f + β j  E ( R M − c M ) − r f  ,

(4.22)

where βj is related to the respective risk covariance,

βj =

cov (r j , r M ) var (r M )

=

cov ( R j − c j , R M − c M ) var (r M )

.

Asset Pricing and Market Liquidity

103

Denote the risk premium on the market portfolio as λ = E ( R M − c M ) − r f. Equivalently, we can express the conditional expected gross return in terms of the market risk premium as E(R j ) = r f + E(c j ) + λ −λ

cov ( R j , R M )

cov ( R j , c M ) var (r M )

var (r M )

−λ

+λ

cov (c j , c M )

cov (c j , R M ) var (r M )

var (r M )

.

(4.23)

his equation is the liquidity-adjusted CAPM developed by Acharya and Pedersen. his formulation states that the required excess return is the expected level of liquidity plus four betas multiplied by the market risk premium. he four betas, deined below, determine the liquidity risk and they depend on the comovement of the security’s payof and liquidity risks with the return and liquidity risk of the market portfolio:

β 1j = β 2j =

β 3j =

β = 4 j

(

cov R j , R M var (r

M

(

)

cov c j , c M var (r

M

)

(

),

cov R j , c M var (r

M

(

)

cov c j , R M var (r

M

)

),

), ).

he factor β j denotes the market beta of the traditional CAPM, but the denominator is adjusted for a term related to trading costs. In the absence of transaction costs, this reduces to the market beta of the tra2 ditional CAPM. Factor β j represents the commonality in liquidity that is the liquidity risk arising from the comovement of security liquidity with market liquidity. According to the model, and supported by empirical results, investors require a return premium for securities that become illiquid when the markets in general become illiquid. 1

104

Market Liquidity Risk

he factor β 3j captures the liquidity risk arising from the comovement of the security return and market liquidity, and the factor β 4j measures the comovement between the security’s liquidity and the gross market return. Economic interpretation of liquidity risk premium he liquidity-adjusted CAPM provides a framework for identifying the channels through which liquidity risk is priced. For the purposes of this discussion, we rewrite equation (4.23) according to a simpliication from Foucault: 26

(

)

E ( R j ) = r f + E (c j ) + λβ 1j + λ β 2j − β 3j − β j . 4

(4.24)

We can further simplify the pricing equation to separate market risk, cap1 LIQ tured by β j , and liquidity risk, captured by β j , E ( R j ) = r f + E (c j ) + λβ 1j + λβ LIQ j ,

(

)

(4.25)

2 3 4 where β LIQ denotes the pricing efect of liquidity risks as j = β j− β j− β j a linear combination of three liquidity betas. Note that the liquidity level premium and the liquidity risk premium are collectively referred to as the liquidity premium in this model. Each of the three risk betas identiies a channel through which liquidity risk is priced. he risk premium, β2, captures the comovement between liquidity of the security and liquidity of the overall market. A high β2 means that the security would typically become illiquid when the overall market becomes illiquid. he average investors will require a higher return on these securities as compensation for this component of liquidity risk. he risk premium β2 therefore captures the pricing efect of the commonality in liquidity or more generally the time-varying common factor in liquidity. his channel shows that it is not just the liquidity of the individual asset that is important, but that shocks that afect the overall market liquidity also afect asset pricing. he capital constraints discussed under the Limits to Arbitrage provide one explanation for the commonality in liquidity. Other studies also support the existence of the commonality in

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liquidity. For example, some studies explain the commonality in liquidity as originating from the inventory risk of market intermediaries or from common market factors such as asymmetric information between market participants.27 he risk premium β 3j measures the efect of the liquidity risk on expected return arising from the sensitivity of the security return and 3 market liquidity. he model shows that a higher value for β j reduces the expected return. A high β 3j implies that the security does well when the 3 market liquidity decreases. In other words, securities with a high β j ofer a hedge against a drop in market-wide liquidity. he asset-pricing efect of this liquidity channel has also been studied extensively by others.28 An interesting empirical example of the detrimental efects of this component of liquidity can be found in the hedge fund LTCM. he fund implemented a strategy whereby they purchased less liquid securities and sold more liquid securities, essentially proiting from the expected return differential of the portfolio. However, when market liquidity deteriorated due to the Russian debt crisis in 1998, LTCM portfolio value deteriorated due to its sensitivity to the liquidity of the debt market. he risk premium β 4j measures the efect of the liquidity risk on expected return arising from the security’s liquidity and the gross market return. Securities with a high β 4j remain liquid when the market goes down. he model shows that this risk premium is negatively related to expected return since investors value a security that can easily be sold in a down market and are willing to accept a lower relative return. When an investor holds securities that are illiquid at a time when the market is down, losses escalate because of losses on illiquid securities. his is well recognized by practitioners: “[b]because there are so few buyers, you’re forced to sell at a discount that is both huge and unpredictable.”29 he pricing implications of β 4j are supported by the research of Markus Brunnermeier from Princeton University and Lasse Pedersen from New York University.30 Brunnermeier and Pedersen showed that liquidity risk is realized through this channel when investors simultaneously hit their funding constraints and are forced to liquidate their positions in a distressed market. A related example of liquidity risk being realized through this channel is asset ire sales, which we discuss in more detail in chapter 6.

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Discussion of selective empirical results 2 Empirical studies agree that commonality in liquidity risk, β j , is the least important component of the liquidity risk premium, while the risk premium related to the comovement between asset illiquidity and market 4 return β j is the most important.31 Professors Björn Hagströmer, Björn Hansson, and Birger Nilsson used a comprehensive data set covering over 80 years of NYSE trading between 1927 and 2010 to study the dynamics of the liquidity premium over time.32 hey decomposed the total liquidity premium into a compensation for the level of liquidity and a compensation for liquidity risk. he level premium is the ratio of the expected liquidity cost and the expected holding period. he liquidity risk premium is related to the sum of the LIQ three liquidity betas β j of the LCAPM deined in equation (4.24).33 he results using the NYSE data showed that the liquidity premium varies substantially over time, with peaks in downturns and crises, but with no general tendency to decrease over time. he professors estimated a liquidity level premium between 1.25 and 1.28 percent per year, and an aggregate liquidity risk premium between 0.46 and 0.83 percent per year. he summation of these two efects gives us the total liquidity premium of between 1.74 and 2.08 percent per year. Both the liquidity level premium and the liquidity risk premium show considerable variation over time. he estimates of each by Hagströmer, Hansson, and Nilsson are shown in Figure 4.4. he time variation of the liquidity level premium (LP), shown in black, and the liquidity risk premium (RP), shown in gray, is evident from Figure 4.4. he relative importance of the liquidity level premium is consistent throughout the sample period. he liquidity risk premium (RP) exhibits signiicant time variation, with peaks during crisis periods. For example, consider the peak during the Great Depression of the 1930s and then again during World War II. Another interesting observation from this data is the high liquidity risk during these earlier periods compared to the more recent 2007–2008 crisis period. Since the 1970s, the magnitude of the liquidity level premium has increased relative to the liquidity risk premium. During the period 1965 to 1975, the liquidity risk premium appeared to follow an increasing trend, and since then it has gradually been

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1.20 1.10 1.00

Liquidity Premium Risk Premium

Percentage per Month

0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

19 2 19 70 3 3 19 00 3 3 19 30 3 3 19 60 3 3 19 90 4 3 19 20 4 3 19 50 4 3 19 80 5 3 19 10 5 3 19 40 5 3 19 70 6 3 19 00 6 3 19 30 6 3 19 60 6 3 19 90 7 3 19 20 7 3 19 50 7 3 19 80 8 3 19 10 8 3 19 40 8 3 19 70 9 3 19 00 9 3 19 30 9 3 19 60 9 3 20 90 0 3 20 20 0 3 20 50 08 3 03

–0.10

Figure 4.4

The liquidity level premium and the liquidity risk premium.

decreasing. Well-known periods of distress, including the oil crises of 1973 and 1979, the October 1987 crash, the LTCM bankruptcy in 1998, and the Lehman Brothers bankruptcy in 2008, are marked by peaks and high volatility in liquidity risk. To explore the liquidity-adjusted CAPM in practice, you have to choose an empirical liquidity measure. Not all liquidity measures are the same, so using a single measure in the model may raise concerns of whether the results are driven by systematic but measure-speciic components or by systematic and common components of measure liquidity.34 If we assume that multiple liquidity measures capture the systematic liquidity, then our choice of measure is not that critical. However, this becomes a question of which empirical liquidity measure to choose, but it does not obviate the theoretical insights aforded by the LCAPM. he original model was illustrated using the Amihud measure. It shows that there exists a systematic common component across eight diferent liquidity measures. It is the systematic common component that is priced, as shown empirically using security market data for the period.35 In this approach, the level of liquidity at a given point in time and the sources of liquidity risk, that is, security liquidity and market liquidity, are exogenous. he model also does not explain why there is commonality

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in illiquidity across stocks or between illiquidity and security returns. In chapter 6, we describe how changes in illiquidity may occur and how commonality liquidity can emerge. Implications for asset pricing and risk management Each of the frameworks we discussed in this chapter highlights a diferent aspect of market liquidity and provides insight into the mechanisms whereby market liquidity afects asset prices. For example, bid-ask spread, the Amihud measure, and the Kyle market impact measures employ principles of market microstructure to measure market liquidity. hese models assume that market liquidity can be captured suiciently as an exogenously added transaction cost. While these measures are easy to implement, and are useful diagnostic tools, they have limited value in pricing securities before the actual trade have been executed. he search-and-bargaining framework provides insight into the driving factors of market liquidity in an intermediated market setting. he framework furthers our understanding of market liquidity that arises endogenously as part of the trading process and the institutional arrangements of trade. he Shleifer-Vishny agency model highlights the importance of capital for trading and quantiies the liquidity premium in the event that capital is constrained. he dynamic and uncertain nature of market liquidity is succinctly captured and quantiied in the LCAPM using a stochastic process to represent liquidity.

Appendix Chapter 4

Derivation of the Search-and-Bargaining Model We derive the bid and ask prices in the search-and-bargain model discussed in the section titled “Liquidity premium of a search-and-bargaining model.”36 he full set of investor types in each period is Ι = {π ho , π hno , π lo , π lno }, where the subscript “l” and “h” indicates the investor’s liquidity state, low or high, and the superscript “o” and “no” distinguishes respectively between owners and nonowners. he per-capita supply of the consol bond is denoted by q. In each period, the fraction of investors willing to no no buy the security is π b = ψπ l + (1 − ψ ) π h , and the fraction willing to sell o o the security is π s = ψπ h + (1 − ψ ) π l . Consider the case in which we have πb > πs. here is excess demand for the security, so its price is determined by the maximum value that buyers place on it. Let πh be the steady-state fraction of high rollers and πl be the steady-state fraction of low rollers. It follows, then, that π h + π l = π ho + π hno + π lo + π lno = 1. Next, posit that in each period a fraction ψ of high rollers becomes low rollers, and vice versa. Hence,

π h = (1 − ψ ) π h + ψπ l = (1 − ψ ) π h + ψ (1 − π h ). 1 Solving this equation for the fraction of high rollers yields π h = . 2 By deinition, π h = π ho + π hno and π l = π lo + π lno. Moreover, all shares are necessarily owned either by high rollers or by low rollers so that q = π lo + π ho. We have the following set of equations: 1 π h = π ho + π hno = , 2

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1 π l = π lo + π lno = , and 2

π lo + π ho = q. Using these equations, ater some straightforward steps, one obtains 1 π b = π s +  − q . 2  1 So πb > πs, if and only, if q < . 2 Let a be the maximum price that an investor is willing to pay and b be the minimum price that a seller is willing to accept from a dealer. We also assume that dealers cannot hold inventories: their aggregate inventory at the end of each period must be zero, and dealers with long positions sell the asset to those with short positions. We assume that these interdealer transactions take place at price:

µ=

a +b . 2

At any ask price a less than a the investor demand for the security exceeds its supply, expressed mathematically as πb > πs, if a = a , buyers are indifπ ferent. We assume that they choose to buy with probability ρb = φ s . In πb this way, the number of buy orders received by dealers in the aggregate is just equal to the number of sell orders, so their aggregate inventory is zero, as required. Hence, the ask price set by dealers a equals buyers’ reservation price a. Dealers’ bid price cannot be determined by the same reasoning, because at any bid price below μ, there is no excess supply of the security. Market makers and sellers bargain over the bid price, producing a bid price that is the average of the dealers’ and sellers’ valuations, weighted by their respective bargaining power: b = zb + (1 − z ) µ , with 0 ≤ z ≤ 1. As dealers’ bargaining power z increases, they extract a larger surplus from sellers. At the limit, for z = 1, the surplus let to sellers is zero.

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To obtain the equilibrium ask and bid prices, we must compute a and b. We irst compute the discounted value of the future stream of cash lows that each type of investor expects to receive just ater trading in a given period. k Let Vj be the discounted value for a trader with valuation j ∈{h, l} and type k ∈{o, no} . Next, consider the high-valuation nonowner who contacts a dealer: he buys the security if and only if Vho − a ≥ Vhno , since otherwise he is better of staying a nonowner. Similarly, a low-valuation owner will sell the security to a market maker if and only if Vlno + b ≥ Vl o . hus, a = Vho − Vhno = nVh and b = Vl o − Vlno = nVl . To determine nVh and nVl , we irst calculate Vjk for j ∈{h, l} and k ∈{o, no}. he value placed by a high-roller owner on the security is o 1 − ψ )Vho ψ (1 − φ )Vl o ψφ (Vl + b ) 1 ( + + + V = . 1+ r 1+ r 1+ r 1+ r o h

(A4.1)

To understand this expression, observe that a high-roller owner always receives $1 with certainty at the beginning of the next period, which explains the irst term. he last three terms are simply the weighted average of the discounted cash lows for the investors in each of the possible states at the end of the next period, the weights being the respective probabilities. With probability (1−ψ), the investor remains a high-roller owner and therefore values the discounted cash low of the o asset at Vh . his explains the second term in the equation. With probability ψ (1 − φ ), he turns into a low-roller owner who does not manage to sell to a dealer, and thus ends up valuing it at Vl o in the subsequent period. his explains the third term. Finally, with probability ψφ , the investor becomes a low roller who does manage to resell the security at price b in the next period. In this state, the investor receives b, but also keeps the option of buying at some point in the future. he value of this o option is Vl . his state is captured by the last term.

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Proceeding in the same way, we obtain the discounted value of future cash lows for a high roller who does not own the asset: Vhno =

b no (1 − ψ ) ρ b(Vho + a ) ψ Vlno (1 − ψ ) (1 − ρ )Vh + + . 1+ r 1+ r 1+ r

(A4.2)

he irst term corresponds to the state in which the investor’s valuation drops, so in the next period he wants to buy the asset. he second term refers to the state in which his valuation stays high, but he does not manage to buy the security from a dealer, and the last term to the situation in which the investor does buy the asset from a dealer (at price a with probability ρb) and therefore owns the asset at the end of the next period. Following the same reasoning, we get Vl o =

(1 − c ) ψ Vho 1+ r

+

1+ r

+

(1 − ψ ) (1 − φ )Vlo + (1 − ψ ) φ (Vlno + b) . 1+ r

1+ r

(A4.3)

and Vlno =

(1 − ψ )Vlno + ψ (1 − ρ b )Vhno + ψρ b(Vho + a ) . 1+ r

1+ r

1+ r

(A4.4)

From equations (A4.1), (A4.2), (A4.3), and (A4.4) we obtain ∆Vh =

(

)

1 + ψ (1 − φ ) ∆Vl + (ψ b + (1 − ψ ) a ) φ − (1 − ψ ) φ − ρ b ( ∆Vh − a )

(1 + r ) − (1 − ψ ) (1 − φ )

.

Recalling that a = ∆Vh, this expression can be rewritten as ∆Vh =

1 + ψ (1 − φ ) ∆Vl + (ψ b + (1 − ψ ) a ) φ

(1 + r ) − (1 − ψ ) (1 − φ )

.

Proceeding in the same way, we obtain ∆Vl =

(1 − c ) + ψ (1 − φ ) ∆Vh + (1 − ψ ) φb + ψφ a . (1 + r ) − (1 − ψ ) (1 − φ )

(A4.5)

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Hence, ∆Vh − ∆Vl =

(1 − c ) + S (1 − 2ψ ) φ , (1 + r ) − (1 − ψ ) (1 − φ )

(A4.6)

where S = a−b is the bid-ask spread charged by dealers. Now, recalling that a = ∆Vh

(A4.7)

and b = bz + (1 − z ) µ

(A4.8)

so µ = a + b = ∆Vh + ∆Vl , we deduce from equations (A4.7) and (A4.8) 2 2 that ∆Vh − ∆Vl =

2 ( a − b ) 2S = . 1+ z 1+ z

(A4.9)

Substituting ∆Vh − ∆Vl , deined in equation (A4.6), into equation (A4.9) and solving for S, we get S=

(1 − z ) c , 2 (r + 2ψ ) − (1 − 2ψ ) φ (1 − z )

(A4.10)

which is the expression shown in equation (4.12) and discussed in detail in the titled “Liquidity premium of a search-and-bargaining model.” his expression shows that the bid-ask spread increases or, equivalently, the market liquidity decreases if the dealer’s bargaining power z and or holding cost c increases.

5

Stories of Liquidity and Credit

Introduction In 1959, Professor Lawrence Fisher1 presented a hypothesis about the determinants of the risk premium on corporate bonds.2 Fisher showed that the average risk premium, deined as the yield diferential between a corporate bond and the risk-free rate, depends on two factors. he irst factor relects the default risk or creditworthiness of the issuer. It captures the basic idea that the lender will get their money back. Incidentally, this factor has dominated our thinking about bonds in general and corporate bonds in particular. But Fisher also considered the “marketability” or liquidity of the bond, deined as the market value of the irm’s outstanding bonds traded in the secondary market, to contribute to the risk premium.3 If Fisher introduced the idea of “marketability” in the middle of the last century, why have we for the most part ignored bond market liquidity? Our lopsided attention to credit risk can be traced back to three factors. First, the risk premium usually dominates during good times, while market liquidity usually takes center stage during times of inancial distress. For example, liquidity of the bond market was of particular concern ater the 1929 railroad crash, in the fall of 1998 ater Long Term Capital Management (LTCM) was liquidated, and most recently in the atermath of Lehman Brothers’ default in September 2008. During September 2008, the yield spread between ten-year government agency bonds and similarduration Treasury bonds was as much as 170 basis points, which was considerably wider than a normal spread of 10 to 30 basis points. Such wide spreads for the highest-quality borrowers pointed to illiquidity rather

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fundamental problems.4 he near-collapse of the commercial paper and repurchase markets efectively sidelined natural buyers of risky debt who relied on these markets to inance their purchases. he second factor is the lack of generally accepted asset pricing models that simultaneously account for the efects of credit risk and market liquidity. he workhorse model for the pricing of defaultable securities is the seminal Merton model, named ater the famous Nobel laureate Dr. Robert Merton. Merton solved the problem of credit risk, or the probability that a irm will be unable to fulill its obligations, assuming a market without impediments to trade.5 Brokerage irm analysts and investors employ the model to determine the company’s ability to service its inancial obligations. But credit spreads obtained from the Merton model typically do not match the market-observed spreads across ratings categories.6 In this discussion, we develop the argument that liquidity could be the missing ingredient in this standard pricing model. he third impediment to the analysis of liquidity is that historically we lacked credible information on the majority of ixed-income trades. Unlike stocks, ixed-income securities such as corporate bonds, collateralized debt obligations (CDOs), and credit default swaps do not trade in a centralized market, but rather in the more opaque over-the-counter (OTC) market. Unlike centralized markets, information on traded prices and volumes is not readily available in an OTC market structure, as discussed in chapter 3. he importance of information is, however, well recognized by academics, regulators, and ixed-income traders. Relecting on liquidity concerns in corporate bonds, the National Association of Security Dealers (NASD) instituted Trade Reporting and Compliance Engine (TRACE), which captures and disseminates consolidated information on secondary market transactions, such as prices and volumes, in publicly traded TRACE-eligible securities (investment-grade, high-yield, and convertible corporate debt).7 In principle, the liquidity premium should relect an investor’s perception of conditions in the secondary markets, and the probability of having to take a large price discount at the point of sale. In reality, bond liquidity is inluenced by a range of factors. hese include long-standing structural

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factors, for example, allocation of foreign exchange reserves in some emerging markets, shits in real money investor preferences, and regulatory requirements, as well as cyclical factors such as heightened risk-taking prompted by the global low interest rate environment and manifested by elevated appetite for credit and liquidity risks in the so-called “search for yield.” Monitoring and understanding the drivers of liquidity risk premia are therefore important because low bond liquidity premia may disguise an underlying fragility of the inancial system as a whole. he 2007–2008 inancial crisis and the sovereign debt crisis in Europe and Argentina demonstrated the importance of the interaction between default and liquidity in inancial markets. Another conundrum for simple discounted cash low models of bond pricing is the opaque link between the liquidity premium and the default premium. For example, during the 2007–2008 inancial crisis, deterioration in debt market liquidity caused severe inancing diiculties for many inancial irms, which in turn exacerbated their credit risk. Even during times of normal market operations, empirical results show that the time variation in bond indices for different ratings exceeded the credit factor and are related to diferences in aggregate market liquidity. For two bonds in the same ratings category, a diference in their market liquidity could result in a diference in their yield spreads.8 Some asset pricing models treat the default and liquidity premia as independent, thus ignoring the interactions between credit risk and market liquidity. Empirical studies demonstrate that interactions between market liquidity and credit risk could afect a irm’s cost of capital through several channels. We discuss the implications of reinancing debt in an illiquid market in this chapter. Understanding the portion of corporate yield spread attributable to default risk versus market liquidity is critical from a corporate inance perspective because it directly afects capital structure issues such as the timing of debt issuances, but it is also of fundamental importance from an investment and risk management perspective. Considering the nature of bond markets (bonds are “real cash”), we can make a compelling argument that theories of bond market liquidity should straddle trading, regulatory policies, and balance sheet strategies, as well as macroeconomic factors such a monetary policy (while similar

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inluences are observed in the equity or derivative markets, the links are more indirect). he frameworks we discuss approach the problem of bond market liquidity, and more speciically the interaction with credit, from these diferent perspectives. Our goal is not to ofer a comprehensive treatment of the vast amount of research that has been published since the recent inancial crisis, but to provide the reader with diferent perspectives that will enhance our understanding of market liquidity.9 We start our discussion with a review of the literature on diagnostic tools of bond market liquidity and then investigate the underlying market mechanisms and corporate strategies that could potentially reduce bond market liquidity. We discuss two general frameworks of quantifying the bond liquidity premium that take credit risk into account. We focus on corporate bonds as quintessential securities to examine interactions between liquidity and credit risk factors, but the principles we discuss apply equally to other ixed-income securities, such as CDOs or assetbacked securities (ABSs) with default risks. According to the Securities Industry and Financial Markets Association (SIFMA), the aggregate corporate US debt outstanding as of September 2014 was approximately $40 trillion. As of the second quarter of 2014, US corporate bond issuance totaled over $410 billion, exceeded only by the issuance of US Treasury debt. Diagnostic tools of bond market liquidity Investors care about liquidity in corporate bonds. here is also considerable variation in the credit quality and liquidity in this market, both over time and across bonds. Prices from standard discounted cash low models do not necessarily match traded bond prices because, for the same promised cash lows, less liquid bonds will trade less frequently, realize lower prices, and exhibit higher yield spreads. Consider the yield diferential between US Treasury and comparable maturity AAA corporate bonds. Between 1926 and 2008, investors paid on average 72 basis points per year for the liquidity and safety attributes of Treasuries relative to corporate bonds. Approximately 46 basis points of the 72 basis point spread account for the liquidity beneits of Treasuries relative to corporate bonds,

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and 26 basis points account for their relative safety.10 As we established in chapter 4, investors demand a liquidity premium for illiquid securities. Liquidity cost inhibits the ease and frequency of trading. Because investors cannot continuously hedge their risk, they demand an ex-ante risk premium by lowering security prices. he OTC market, in which the majority of corporate bonds are being traded, is fundamentally diferent from the exchange-traded market, compelling us to go beyond standard market microstructure transactionbased measures frequently used in exchange-traded markets to monitor bond market liquidity. One such measure is the bid-ask spread, which could range from as little as 3 basis points to as much as 150 basis points, according to some studies.11 Empirical results show that liquidity in corporate bonds is signiicantly greater than what can be explained by bidask spreads alone.12 Even though it is oten referenced, the bid-ask spread is generally only available for relatively larger bond issuances, and it is a one-dimensional measure that does not relect aspects of market liquidity such as the market depth, in other words, the size of the transaction that can be absorbed without afecting the price, or the speed with which orders can be executed. he bid-ask spread typically does not suiciently capture important drivers of market liquidity in the OTC market, such as search frictions, bargaining power, or inventory holding costs, and it is therefore not a well-suited metric in the OTC market. Despite these limitations, the bid-ask spread is still being used due to a lack of consensus on how to price and monitor liquidity in corporate bonds. In the next three sections, we discuss some alternative diagnostic tools that risk managers, portfolio managers, and regulators can use to monitor bond market liquidity. Corporate bond-credit default swap basis he bond-credit default swap basis compares the yield spread of a corporate bond over the risk-free interest rate and the corresponding credit default swap premia on the same reference entity, same maturity, same seniority, and same currency. Corporate bonds and the corresponding credit default swaps, or reference basket of corporate bonds, carry the

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same credit risk, so the basis should relect the relative compensation required by investors for bearing other noncredit risks, such as the liquidity premium priced in the corporate bond market.13 A common measure of the risk-free benchmark is interest rate swaps, but some also use the Treasury yield with a maturity matching that of the corporate bond. No-arbitrage requirements stipulate that whenever the bond-credit default swap basis is suiciently diferent from zero, it is theoretically possible to implement a basis trade, selling (buying) credit risk in the bond market and buying (selling) credit risk in the derivative market using a credit default swap. For this relationship to hold or, put diferently, for the basis trading strategy to be proitable, markets should be relatively liquid with narrow bid-ask spreads, funding for bond purchases should be unconstrained, and the interbank market should function eiciently.14 In general this arbitrage is not perfect due to technical reasons, such as the cheapest-to-deliver option of the credit default swap and the practical challenges that are involved in short-selling bonds, factors that tend to render the basis slightly positive during liquid markets.15 Academic research, supported by empirical results, shows that a reason for the divergence in the bond-credit default swap basis is illiquidity in the corporate bond market.16 he bond-credit default swap basis is used to monitor the liquidity premium in the corporate bond market. he relative simplicity of this approach is appealing. Because it ignores other factors that can also afect the basis, this approach can lead to a biased estimate of liquidity. For example, when bank liquidity is scarce, credit default swap spreads may also include an allowance for counterparty credit risk, and so it is not necessarily a clean measure. Credit default swaps may also have embedded liquidity risk that biases the default component, which in turn leads to a bias in the measure of the liquidity premium itself.17 Liquidity measure based on return covariance: the modified Roll measure he proponents of the random walk argue that changes in the price of the current transaction will not be inluenced by the sequence of preceding

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price changes. Randomness of successive price changes is, however, not relected in real-world transactions. In 1966, the hedge fund manager and New York Times best-selling author Victor Niederhofer, studying the ticker tape of traded prices as published by Francis Emory Fitch, Inc., observed that successive price changes show considerable dependence.18 He provided empirical evidence of nonrandomness in stock prices and further observed that there was a general tendency for price changes to reverse. In the language of an order book, price changes tend to reverse when order low is balanced. In other words, trades on the bid side are followed by trades on the ask side of the market. he relevance of Niederhofer’s observation for bond markets lies in his recognition of the link between price reversals and market liquidity. Price reversals give rise to transitory efects in traded prices. Niederhofer’s insight that the magnitude of transitory price movements relects the degree of market liquidity forms the basis for the modiied Roll measure. he modiied Roll measure provides a simple framework of market liquidity. Assume that the fundamental value Vt follows a random walk. Express the observed transaction price as Pt = Vt + ut .

(5.1)

he second component, ut, represents the transitory component, and it is assumed to be uncorrelated with the fundamental value. In this framework, the magnitude of the transitory price component ut characterizes market liquidity and more speciically in this model it refers to the level of illiquidity in the market. We can extract the transitory component of the transaction prices by computing the covariance of observed changes in transaction prices:

γ = −cov ( ∆Pt , ∆Pt −1 ) .

(5.2)

Because transitory price movements lead to negatively serially correlated price changes, the negative of the autocovariance in relative price changes, denoted by γ, gives a meaningful measure of illiquidity. A greater numerical value of γ indicates a less liquid market. In general, γ depends on the horizon over which price changes are measured. he horizon efect is important because γ measured over diferent horizons may capture different aspects of the transitory price components. For example, using

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Market Liquidity Risk

trade-by-trade prices or end-of-day prices in estimating γ captures more of the high-frequency components in transitory price movements. he measure can be recognized as an extension of the Roll measure we discussed in chapter 4, but unlike the Roll measure, it does not restrict the covariance to the efective bid-ask spread. he relationship between the Roll measure and the modiied Roll measure is SRoll = 2 γ .

(5.3)

Equation (5.3) quantiies the underestimation of liquidity using the Roll measure. Because the modiied version in equation (5.2) is less restrictive in its assumptions than the original Roll measure, it captures the broader impact of liquidity on prices beyond the efect of bid-ask spread by relying on transaction prices. Another advantage of the modiied Roll measure is that it does not rely on any particular bond-pricing model. Professor Jack Bao of he Ohio State University, and Professors Jun Pan and Jiang Wang of Massachusetts Institute of Technology employed the modiied Roll measure to examine the pricing implications of market liquidity on corporate bonds spreads. heir study quantiied the relative importance of liquidity and credit using transaction-level data from 2003 through 2009.19 Using the modiied Roll measure as a diagnostic tool, Bao, Pan, and Wang provide valuable insight into the dynamics of bond market liquidity and in particular the interaction between liquidity and credit. he empirical results explain the crosssectional variation of liquidity at the bond level, ater controlling for credit risk using either credit default swap spreads or ratings as a proxy for credit risk. During normal market conditions, liquidity and credit are equally important drivers of high-rated yield spreads. However, during the 2008 crisis, liquidity was by far the most important factor in explaining the monthly changes in the US aggregate yield spreads of high-rated bonds (AAA through A). he results show that for two bonds in the same rating category, a one standard deviation diference in the market liquidity of these bond leads to a diference of up to 65 basis points in their yield spreads. Bao, Pan, and Wang’s results show that the modiied Roll measure is robust and that magnitude and statistical signiicance of this measure

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persist even ater controlling for proxies of bond liquidity such as the average trade size or the age of the bond. Regression model using liquidity proxies Why limit ourselves to a single liquidity measure? Given the multifaceted nature of bond market liquidity, we could combine various liquidity measures as explanatory variables in a linear regression model. he framework of a regression model also allows us to separately control for credit risk. Opponents of regression models can rightly point to two potential problems with this framework. First, there is the issue of multicollinearity when using somewhat related measures as independent variables. However, according to comprehensive empirical research by Nils Friewald et al.,20 the various liquidity proxies measure somewhat diferent aspects of liquidity empirically, rendering the issues of multicollinearity less severe in this application. he second objection is that the simplicity of a linear model does not capture the nonlinear tail dependencies typically present during times of inancial distress. Regression models can overcome this limitation by analyzing periods of inancial distress and normal periods separately.21 Within the framework of regression models, the possible variations are endless. We base our discussion of the prominent features of regression models on the 2012 study done by Professors Friewald and Rainer Jankowitsch of the Vienna University of Economics and Business, and Professor Marti Subrahmanyam of New York University.22 Friewald, Jankowitsch, and Subrahmanyam relied on a panel regression to analyze changes in bond yield spread as the dependent variable, and trading activity and liquidity measures as the explanatory variables:23 ∆(Yield Spread)i ,t = ao + β1 ∆(Yield Spread)i , t −1 + β2 ∆(Trading Activity Variables)i , t + β3 ∆(Liquidity Measures)i , t + β4 ∆(Rating Dummies)i , t + ε i , t .

(5.4)

he rating class dummies are added to the model to control for efects related to credit risk. he irst lag of the yield spread change represents an

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Market Liquidity Risk

observed autoregressive component in spreads. Trading activity variables include metrics such as the number of trades, trade volume, and trading interval. Higher trading activity is usually associated with more liquid markets. Longer trade intervals indicate less trading activity and therefore lower liquidity. Liquidity measures include the Amihud and Roll measures discussed in chapter 4, as well as a price dispersion and zero-return measure.24 Empirical results suggest that among the liquidity measures, the Amihud and price dispersion measures are statistically more signiicant. Among trading activity variables, changes in the volume and trading interval are statistically more signiicant than the number of trades. Indirect proxies of market liquidity based on bond characteristics such as the coupon, age, issued amount, and bond covenants could also be included in the model.25 In general, the liquidity of a particular bond decreases with the bond’s age and maturity, but increases with its issuance size.26 Friewald et al. applied this regression model to a comprehensive set of corporate bond trading data between 2004 and 2008. Liquidity efects accounted for approximately 14 percent of the explained market-wide changes in corporate bond yield spreads. he Amihud measure was economically the most important explanatory variable among the variables considered. he study also found that relying on simple bond characteristics and trading activity variables captures idiosyncratic information, but if used alone, these measures will not suiciently capture market liquidity. Measures such as the Amihud and Roll measures that rely on traded prices and volumes use trading information more efectively, and the Friewald et al. study found them to be superior measures of market liquidity. Regression-based approaches are generally useful in conirming the existence of a liquidity premium that varies over time, and the popularity of these methods in empirical work lies in their simplicity. However, a direct link between regression betas and bond prices has not been established. A further limitation of the regression approach is that it requires a large number of observed trades that may not be readily observable. Implicit in the regression model is the assumption of a linear relationship between credit and liquidity, which may not necessarily be true in realword market transactions.

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Empirical evidence of bond market liquidity and linkages to credit risk Improving the existing asset pricing models of defaultable securities naturally forces us to develop a deeper understanding of the relative importance of liquidity and credit risk and how their importance varies with market conditions. It is informative to use the yield spread of corporate bonds over comparable maturity Treasury bonds as the discussion metric in this section. An ignorant observer may assume that the yield spread represents default risk and accordingly draw conclusions regarding default probabilities from spreads. But these estimated default probabilities do not agree with empirical data. he yield spreads are typically wider than justiied by historical default losses and may not be as informative about default risk as is oten assumed. While the concept of a spread is easily described, the historical properties of corporate bond markets show that spreads are compromised. Bond yield spreads also relect noncredit factors such as liquidity and to some extent also macroeconomic activity.27 Another key observation in the liquidity-credit story is that bonds have inite maturity, giving rise to the notion of a term structure of bond yields. he term structure simply means that bonds with diferent maturities can have diferent yields depending on the shape of the term structure. For example, if the yield curve is upward sloping, the yield on a short-term bond will be lower than the yield on a long-term bond. he Nobel laureate Robert Merton related the shape of the term structure to the probability of default of the issuer: high-grade corporate issuers face upward-sloping credit yield curves, while speculative-grade irms’ credit yield curves are downward sloping or hump-shaped (i.e., mostly downward sloping).28 An empirical study of the intricate dynamics between market liquidity and default risk was undertaken by Professors Long Chen, David Lesmond, and Jason Wei, who analyzed 4,000 corporate bonds spanning both investment-grade and speculative categories over a nine-year period between 1995 and 2003.29 hey used bond rating as a proxy for default risk (a related study was done by Professor Francis Longstaf, but he used a much smaller sample of only 68 issuers and used credit default swaps as proxy for default risk30). he most interesting inding of Chen, Lesmond,

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Market Liquidity Risk

and Wei’s study is the consistent signiicance of market liquidity in explaining bond yield spreads. his holds true across both investmentgrade and speculative-grade bonds. An improvement in market liquidity coincided with a reduction in yield spreads for both investment-grade and speculative-grade bonds. hese empirical results also highlight a dependence between liquidity and credit. Lower-rated bonds are more illiquid. In particular, for investment-grade bonds, liquidity decreases when moving from AAA bonds to BBB bonds. For speculative-grade bonds, the trend of decreasing liquidity with increasing default risk was less apparent. he empirical results show two general patterns: corporate bonds with higher credit ratings tend to be more liquid, and corporate bonds are less liquid during economic downturns. his holds true particularly for riskier bonds. Summary statistics classiied by bond maturity show an increase in costs as we move from short- to long-maturity bonds.31 hese observations are consistent with the more general investment horizon argument, which we discussed in chapter 4. For inite-maturity securities such as bonds, investors can receive the redemption value at maturity without incurring liquidation costs. If investors knew exactly when they would liquidate investments, in other words, if their holding periods were certain, they could match the maturity of their corporate bond investment with their investment horizon. Empirical evidence presented by Amihud shows that there is no perfect match between investors’ investment horizon and securities’ maturity. Instead, investors expect that, with a positive probability, they need to sell securities before maturity, at which time they will incur additional transaction costs. It follows that a inite maturity security is likely to incur transaction costs through its life, and that these costs can be incurred repeatedly because each buyer has these expectations anew, giving rise to a liquidity diference with maturity.32 In general, an investor with an uncertain horizon faces a trade-of between buying short-maturity securities, which may expose him to reinvestment risk, and long-maturity securities, which may result in the payment of transaction costs when selling before maturity. Liquidity risk and aggregate market conditions he time variation of corporate bond liquidity exhibits a substantial level of commonality, indicating that it is a rather important systematic risk

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component, establishing that liquidity risk should indeed factor in asset prices and asset pricing models. Bond market liquidity commoves in an important way with aggregate market conditions. Bao, Pan, and Wang performed a regression analysis of monthly changes in bond market liquidity, measured using the modiied Roll measure discussed above, and changes in the Chicago Board Options Exchange Volatility Index (CBOE VIX). he regression coeicients were statistically signiicant, with an r-squared of around 67 percent for the period 2003 through 2008. he regression coeicients were also statistically signiicant if the sample excludes the 2008 crisis period (the r-squared of around 33 percent).33 his is an intriguing result showing a close relationship between the CBOE VIX and bond market liquidity. he VIX captures the pricing of the Standard & Poor’s (S&P) 500 index options, oten referred to as the “fear gauge” of the market. his study indicates a nontrivial interaction between shocks to bond market liquidity and shocks to the appetite for risk. Quantifying the liquidity premium in defaultable bonds Assume you invested in a zero-coupon bond that promises to pay $1 at a maturity date T. If the issuer of the bond is default free, and market liquidity is negligible, the price of the zero-coupon bond is simply the discounted present value of $1, which can be calculated using a risk-free interest rate to account for the time value of money. Further assume that the issuer of the bond has some credit risk and that there is a possibility that the issuer may default prior to the maturity date T. In addition to the efect of the time value of money and the uncertainty of future interest rates, both the magnitude and the timing of the cash low paid to the investor may be uncertain. For ease of exposition, we can view a defaultable zero-coupon bond as a portfolio of two securities: a security that pays $1 at date T contingent on the issuer surviving to the maturity date, and a security that pays an amount equal to the recovery in the event of a default before the maturity date.34 he irst order of business would therefore be to incorporate the probability of an issuer default in the bond-pricing model. However, as discussed in chapter 4, market

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Market Liquidity Risk

liquidity induces a liquidity premium (or discount) that causes the price of a security more generally to deviate from its fundamental value. Developing an asset pricing model for defaultable bonds that allows markets to be illiquid is challenging because it calls for the simultaneous treatment of credit risk and market liquidity. We discuss two frameworks for pricing of defaultable securities that difer in their treatment of credit risk. he pricing of market liquidity is incorporated into both frameworks in the form of a discount rate or an efective discount rate adjusted to account for market liquidity. he pricing of credit risk has evolved around two formulations of irm default. he structural framework starts with assumptions of the irm’s capital structure, in particular the irm’s outstanding debt and equity. he model assumes that default is based directly on the issuer’s ability or willingness to pay its liabilities, which in turn depends on the value of its assets. he structural framework postulates a process of the evolution of the irm’s asset value over time. he structural model is intuitive—if an analyst wants to understand the impact on credit quality of increased borrowing, share repurchases, or the acquisition of another irm, the structural model naturally lends itself to understanding the implications of these. he reduced-form framework assumes that a irm’s default is unpredictable and that information about the timing of default and expected recovery is not readily available. he reduced-form framework postulates an exogenously speciied process for the evolution of default probabilities. he speciication of the reduced-form framework is mathematically elegant but rather abstract—the default probability is deined as a stochastic process—which does not have the intuitive relationship to the irm’s capital structure that we found in the structural framework. Given the nonintuitive nomenclature of the structural and reduced-form credit models, it is helpful to distinguish between these in terms of the information set that each relies upon: the reduced-form framework utilizes information from the markets, such as traded security prices, while the structural model utilizes information generally available to a irm’s management or regulators, such as detailed information about assets and liabilities.35

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Reduced-form model with liquidity A reduced-form credit models assumes that default is unexpected and not induced by market observables or economic fundamentals. he reducedform model we discuss in this section incorporates market liquidity, and it further assumes that credit and liquidity risks are independent.36 he assumption of independence allows us to separately measure the credit and liquidity components of the bond price. We can employ this reducedform framework to quantify market liquidity or other nondefault-related components in corporate bond prices. Modeling default risk

To capture the uncertainty in the timing of the issuer default, we assume that default is stochastic and we further assume that it can be modeled as a risk-neutral intensity process.37 he value of the zero recovery defaultable bond in terms of the intensity process λt and the interest rate rt is shown in equation (5.5) (we assume that expectations are risk neutral):  − ∫ (ru + λu )du  . P (t , T ) = Et e 0     T

(5.5)

A well-known model that would accommodate a nonnegative default intensity is the square-root or Cox-Ingersoll-Ross (CIR) model under which the default intensity has a noncentral chi-square distribution. he risk-neutral dynamics of the intensity process λt are deined as a Cox-Ingersoll-Ross process: d λt = (α − βλt ) dt + σ λt dZ λ ,

(5.6)

where α, β, and σ are positive constants, and Z is a standard Brownian motion. hese dynamics allow for both the mean reversion and conditional heteroskedasticity. Modeling liquidity

Following the model proposed by Professors Francis Longstaf, Sanjay Mithal, and Eric Neis,38 we assume that the risk-neutral dynamics of

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Market Liquidity Risk

the liquidity process γt can be modeled as a simple Ornstein-Uhlenbeck stochastic process: dγ = η dZγ ,

(5.7)

where η is a positive constant and Zγ is a standard Brownian motion. hese dynamics allow the liquidity process to take on both positive and negative values. Professor Robert Jarrow developed an argument to justify the selection of the Ornstein-Uhlenbeck stochastic process to model market liquidity.39 Jarrow’s argument is consistent with no-arbitrage opportunities in an incomplete bond market, such that, one cannot synthetically construct a particular defaultable zero-coupon bond. Further assume that the market is illiquid, so one cannot readily buy or sell the bond. he price of the bond will accordingly be diferent than the fundamental value due to market liquidity efects. In particular, assume that the following no-arbitrage relationships hold between the fundamental value, V(t, T), of the bond and the trade price, p(t, T) of the bond. he irst case represents an illiquid market in which one cannot readily buy the bond. V (t , T ) ≤ p (t , T ), assuming that there is a reduced supply of bonds. he second care represents an illiquid market in which one cannot readily sell the bond. V (t , T ) ≥ p (t , T ), assuming that supply exceeds demand of bonds. A simple Ornstein-Uhlenbeck stochastic process γt succinctly captures these relationships. More formally we have V (t , T ) = e − γ t p (t , T ) . In a shortage, one cannot readily buy the bond, or put diferently, γt ≤ 0, and the function is positive. his case is analogous to positive convenience yields associated with the storage of production commodities, such as oil. It is advantageous to hold a bond if the demand is greater than the supply of the bond.

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When a glut exists and one cannot readily sell the bond, γt ≥ 0 and the function −γt is negative. his case is analogous to negative convenience yields associated with storage of spoilable commodities such as wheat. Asset pricing model

he asset pricing model is simpliied by assuming that each of the stochastic processes for λt and γt evolve independently of each other.40 he value of the zero recovery defaultable bond with liquidity risk is  − ∫ (ru + λu + γ u )du  . P (t , T ) = Et e 0     T

his equation makes it clear that under this formulation, liquidity is accounted for as an adjustment to the discount rate. his model provides tractable formulas that can easily be expanded to price a coupon bond. Let c denote the coupon rate for a corporate bond, assumed for simplicity to pay coupons continuously. Further assume that the bondholder recovers a fraction δ of the par value of the bond in the event of default of the issuer.41 he price of the coupon bond with maturity T is expressed as T  T − t (ru + λu + γ u )du    − ∫ (ru + λu + γ u )du  ∫    P t , T , δ = Et c ∫e 0 dt   + Et e 0           0 

(

)

 T − t (ru + λu + γ u )du   ∫   + Et δ ∫λt e 0 dt   .       0 

(5.8)

he irst term in the expression in equation (5.8) is the present value of the coupon payments, the second term is the present value of the principal payment, and the third term is the present value of the recovery in the event of a default. Notice that each of these component cash lows is discounted at the adjusted discount rate (rt + λt + γt) that incorporates the liquidity and default risks. he assumption of independence between the liquidity OrnsteinUhlenbeck stochastic process and the square-root dynamics of the

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Market Liquidity Risk

intensity process γt, allow us to simplify the pricing formula in equation (5.8).42 he bond-pricing formula becomes, T

B B (t , T , δ ) = c ∫At Ct e −( t λ ) Dt e − λt dt + AT CT e − (BT λ ) DT e − λT 0

T

− B +δ ∫Ct e ( t λ ) Dt (Gt + H t λ ) e − λt dt , 0

(5.9)

where λ and γ denote the current (the time-zero) values of the default intensity and liquidity processes, respectively, and where

(

)

 α β + φ  1 − κ 2α2 At = exp  t ( )σ , 2 φt  1 −κ e  σ Bt =

β −φ 2φ , + 2 2 σ σ (1 − κ e φ t )

 η2t 3  Ct = exp  ,  6  Gt =

α  α (β + φ )  1 − κ σ2 2 +1 α φt ( ) , e − 1) exp  t (  1 − κ e φ t  σ2 φ

(

)

 α β + φ + φσ 2  1 − κ 2α2 + 2 )σ , H t = exp  t ( φt σ2  1 −κ e 

φ = 2σ 2 + β 2 , and κ = (β + φ ) /(β − φ ). Comments on Model Implementation

In order to apply the pricing model, we need to augment the framework with information on the credit risk of the defaultable security. he assumption of independence between the default and liquidity processes allows us to solve the parameters of the default process directly using information from credit default swaps.43 he remaining parameters can be solved by applying a least-square optimization procedure to the traded bond price. A similar methodology

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can be followed to estimate the parameters using a speciication in which we it the yield of traded bonds, rather than their prices. In the event that a term structure of credit default swap spreads is readily available for the particular bond issuer, it is also feasible to solve for the probability of default directly from the term structure of credit default swap spreads.44 his methodology is more direct, and it does not need any additional assumptions regarding the distributional properties of the default process. he latter approach also has the advantage that it preserves the information of the credit default swap term structure, which is particularly useful in applications such as solvency analysis, risk management, and corporate valuation. he reduced-form model we presented is useful because it ofers a tractable pricing formula that can be solved relatively easily using information from traded bonds and credit default swap spreads. A limitation of this approach is that it ignores the intrinsic interaction between credit risk and liquidity, which may lead to an underestimation of the liquidity premium.45 he pricing framework we discuss in the section titled “A structural credit model with bond market illiquidity” incorporates the dependence between credit and liquidity into the pricing formula. A structural credit model with bond market illiquidity Structural credit models are based on insights from Robert Merton formalized as the seminal Merton model. he Merton model assumes that the irm’s assets, inanced with a combination of equity and debt, evolve randomly over time and that claims on the irm’s assets can be valued using option-pricing methods.46 he model further assumes the irm issued a single zero-coupon bond and that the irm might default at maturity of the bond if the total assets are insuicient to meet the obligations due at the bond’s maturity. he model assumes that the market value of the irms’ assets varies over time, and that this variation is uncertain (random)—the model does not specify the reason for such changes, which makes it applicable to a broad range of inancial and noninancial irms. We capture these assumptions mathematically by assuming that the market value of the irms’ assets follows a log-normal difusion.47 he classical Merton model further

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Market Liquidity Risk

assumes that markets are frictionless, in other words, trading it is not plagued by transaction costs and other issues related to market liquidity. he irm’s equity is priced with the Black-Scholes formula as though it is a call option on the total assets value of the irm, struck at the face value of debt.48 he value of debt is then simply obtained by subtracting the equity option price from the initial asset value. Based on the idea of valuing claims on a irm’s assets using option pricing, a class of models developed that introduced more realistic assumptions. Corporate bond investors require compensation for the uncertainty in the timing of default and the uncertainty in the size of losses due to default (loss is largely determined by the level of the default boundary, deined as the level of assets when default occurs). In “irst-passage” models, spearheaded by the famous economists Fisher Black and John Cox, default occurs when assets drop to a level suiciently below the default boundary, whether or not this occurs at the maturity of the debt.49 Extensions by Professors Hayne Leland and Klaus Tot introduced taxes and bankruptcy costs, which allowed a formal characterization of the optimal capital structure of a irm with endogenous bankruptcy.50 he Leland and Tot formulation allowed the irm to choose the maturity and the amount of debt it issues. his introduced the idea that when a bond matures, the irm usually issues a new bond to maintain their capital. he market price of the new issuance can be lower or higher than that of the maturing bond. he irm’s equity holders absorb the gain or loss due to debt rollover, so the equity value of the irm is determined not only by the value of the unlevered irm but also by the expected future rollover efects. When the equity value drops to zero at the default boundary, the bondholders recover a discounted asset value. Professors Zhiguo He and Wei Xiong further extended the framework by introducing an illiquid secondary bond market.51 hey assume that a bond market investor is exposed to an exogenous market liquidity shock, upon which the holder of the bond is forced to sell his holdings. he sale occurs at a discount to the fundamental value of the bond in a frictionless market. He and Xiong introduce the interaction between market liquidity and credit risk by assuming that the irm reinances or rolls over maturing debt. In the event that the reinancing occurs in an illiquid debt market,

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the irm sufers a loss, which causes the irm to default at a higher fundamental threshold, even without constraints to the irm’s ability to raise more equity. he extension by He and Xiong provides fertile ground for studying the interaction between credit risk and bond market liquidity. Our discussion closely follows that of He and Xiong—we refer to their model as the structural HX model.

Illiquid bond markets and credit risk Assume that a bond market investor is exposed to a market liquidity shock, which arrives according to a Poisson occurrence with intensity ξ.52 Upon the arrival of the liquidity shock, the bond investor has to exit the investment by selling his bond holding in the secondary market at a cost κ. he transaction cost could represent the bid-ask spread charged by dealers discussed in chapter 4. he bond’s value will be lower than the value in a frictionless market.53 Further assume that the irm replaces maturing debt to maintain its capital structure. A key feature of this model is that maturing debtholders are paid in full, while the equity holders of the irm bear the rollover gains and losses. Assume that gains are paid out to equity holders, losses are paid by issuing additional equity, which dilutes existing shares. Consider a simple example to illustrate these concepts. Suppose a irm has one billion shares of equity outstanding, and each share is initially valued at $10. he irm has $10 billion debt maturing. Assume that the bond market experiences an unexpected decrease in liquidity at the maturity of the bonds so that the same amount of bonds is worth only $8 billion due to a negative liquidity premium. To cover the $2 billion shortfall, the irm issues more equity. he proceeds from the share ofering are used to fully pay of the maturing debtholders, but the new shares dilute the existing shares and thus reduce the market value of each share. If the irm only needs to roll over its debt once, then it is easy to compute that the irm needs to issue 1/8 billion shares, and each share is valued at $8. he $2 price drop relects the rollover loss borne by each share. If the irm needs to roll over more debt in the future and the debt market liquidity problem persists,

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Market Liquidity Risk

the share price should be even lower due to the anticipation of future rollover losses. he example illustrates that the equity value is jointly determined by the irm’s fundamental value and expected future rollover gains/losses. Equity holders are willing to buy more shares and bail out maturing debtholders as long as the equity value is still positive, or put diferently, the option value of keeping the irm alive justiies the expected rollover losses. We ignore any additional frictions in the equity markets such as transaction costs and asymmetric information and assume that the irm is not limited in the amount of equity it can issue. he value of the equity is, however, afected by the irm’s debt rollover losses. Asset pricing model Assume that the assets of a generic irm, inancial or noninancial, change over time and that such changes are random. We do not explicitly deine the origin of such changes. he evolution of assets over time At is dAt = (r − δ ) dt + σ dZt , At

(5.10)

where r is the constant risk-free rate (assumed to be constant and exogenous), δ is the irm’s constant cash payout rate, σ is the constant asset volatility, and {Zt : 0 ≤ t ≤ ∞} is a standard Brownian motion, representing random shocks to the irm’s value. he irm’s assets are inancing with a combination of inite maturity debt and equity. At each point in time, the irm has a portfolio of m bonds outstanding. Each bond has a inite maturity of τ, an annual coupon payment c, and a principal value of p. he irm commits to maintaining a stationary debt structure, so a maturing bond will be replaced by issuing a bond with the same principal value, maturity, and coupon. Expirations of the bonds are uniformly distributed over time. During each time interval (t, t + dt), a 1 fraction m dt of the bonds mature and needs to be rolled over. Due to the liquidity risk of the bond market, the market price of the new bond issued to replace the maturing bonds can be higher or lower than the required

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principal on the maturing debt. We assume that equity holders bear the gains or losses from the debt rollover. An investor in the irm’s bonds requires compensation for several risks involved in holding the bond. he investor’s expected return from holding the bond is determined by the cash low from the coupon payment, which is ofset by the potential loss caused by the occurrence of a market liquidity shock, the time decay due to a change in time-to-maturity τ and the potential default of the issuer irm due to changes in the value of the irm’s assets At. he required (dollar) return from holding the bond can be expressed as a partial diferential equation: ∂d( At , τ ) ∂d( At , τ ) + (r − δ )At ∂τ ∂A 2 1 ∂ d( At , τ ) . + σ 2 At2 ∂A 2 2

rd( At , τ ) = c − ξ kd(At ,τ ) −

(5.11)

he terms on the right-hand side represent the contributors to the expected return from holding the bond: the coupon payment c, the loss due of a market liquidity shock, the time decay or loss due to a change in the time-to-maturity τ, and the efects due to changes in the value of the irm’s assets At, shown in the last two terms. he liquidity shock hits with probability ξdt. Upon its arrival, the bondholder is forced to sell their holding, and they sufer a loss due to transaction cost of kd(At, τ). To obtain additional insight into the efect of market liquidity, we rewrite equation (5.11) as

[r + ξk ] d ( At , τ ) = c −

∂d ( At , τ ) ∂τ

+ (r − δ ) At

∂ d ( At , τ ) 1 . + σ 2 At2 ∂A2 2

∂d ( At , τ ) ∂A

2

(5.12)

he formulation in equation (5.12) shows that market liquidity is incorporated as an efective discount rate (r + ξk), which will efect the valuation of the bond. To price the bond, we can utilize the following two conditions. he irm defaults when its equity value drops to zero at an endogenously

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Market Liquidity Risk

determined threshold AB. At default, the bondholders are entitled to the liquidation value αAB, which is usually below the face value of the debt. More formally, we can express these two conditions in terms of so-called boundary conditions: (a) At the default threshold AB, bondholders share the irm’s liquidation αV value proportionally, thus d(Vt , τ ; VB ) = mB , for a recovery rate of α. (b) At maturity of the bond, the holder gets the principal value p if the irm survives, d(Vt , τ ; VB ) = p , for all Vt > VB. To determine the bond price, we apply the boundary conditions speciied in (a) and (b) to solve the partial diferential equation in equation (5.12). he value of the bond according to the HX structural model is deined as:54 d ( At , τ , AB ) =

 c   p − r + ξk  (1 − F (τ ))   α A c  + B − G (τ ) . r + ξk   m c + e − (r + ξk )τ r + ξk

(5.13)

where F(τ) is the cumulative distribution function of the irst passage to bankruptcy and (1 − F (τ )) represents the probability of survival. he bond-pricing equation contains the usual elements, the time-to-maturity τ, the discounted coupon payment, principal payment in the event that the irm survives until the bond matures, and the recovery payment in the event of the irm’s default. he novel feature of this model is that each of these components is discounted at an efective discount rate (r + ξk) that incorporates a liquidity premium ξk. he bond credit spread, deined as the diference between the yieldto-maturity, assuming the irm survives until bond maturity, and the risk-free rate, contains both a liquidity premium and a default premium because the bond price in equation (5.13) includes the efects of the irm’s potential bankruptcy and the efects of market liquidity. he relationship between the yield-to-maturity y and the bond price is d ( At , τ ) =

c 1 − e − yτ ) + pe − yτ . y(

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he HX model renders two channels of interaction between market liquidity and bond pricing. he irst channel through which market liquidity efects transpire is directly as an increase in the liquidity premium kξ. If either the cost of trading increases or the intensity of the liquidity shock to the market (i.e. ξ) or both increase, then bond prices decrease. We discussed many factors earlier that can increase the cost of trading. he intensity of a liquidity shock can increase, for example, if funding is constrained due to increases in margin risk or if funding is less secure due to large-scale investor redemptions. he second channel is more indirect and market liquidity efects transpire when debt needs to be reinanced (so called rollover risk). In the event that the liquidity premium increases, the price of newly issued bonds are suppressed, causing an increase in equity holders’ rollover losses. As a result, equity holders become more reluctant to keep the irm alive even though the falling bond price is caused by deterioration in market liquidity rather than the irm’s fundamental value. he default threshold increases, which in turn leads to a greater default premium in the credit spread. Numerical example of a structural model with market liquidity

To illustrate the efect that a change in the intensity of the liquidity shock or the transaction cost has on the value of the bond, consider the following numerical example adapted from He and Xiong. Consider the efects of a deterioration in market liquidity on credit risk: we consider two irms with diferent credit ratings and compare the change in the bond yield spread of these irms relative to changes in bond market liquidity. We select irms with two representative credit ratings: an A-rated investmentgrade irm and a BB-rated speculative-grade irm. For each credit rating, assume each irm has outstanding debt with maturities of one, three, six, and ten years. Using the formulations of the bond price in equation (5.13), we also specify the following values for the respective parameters, namely the asset volatility and the bond-trading cost. Assume that the investment grade irm has an asset volatility of 21 percent and a bond-trading cost of 0.5 percent (50 basis points) while the speculative grade irm has an asset volatility of 23 percent and a bond trading cost of 1 percent (100 basis points). For each A-rated irm, we calibrate its leverage so that the irm

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Market Liquidity Risk

issues new bonds at par and these bonds have a credit spread of 100 basis points at issuance. For each BB-rated irm, we calibrate its leverage so that its newly issued par bonds have a credit spread of 330 basis points. We report the yield spread for a baseline of liquidity shock of one, but also consider liquidity shocks of two and four. An increase in the liquidity shock from one to four is representative of a severe crisis shock, similar to what was experienced during the 2008 inancial crisis. If the liquidity shock increases from one to two, the liquidity premium doubles from 100 basis points to 200 basis points for the BB-rated irm and from 50 to 100 basis points for the A-rated irm. If the liquidity shock increases from one to four, the liquidity premium quadruples. he results of this numerical example are shown in Table 5.1 for the speculative-grade irm and in Table 5.2 for the investment-grade irm.

Table 5.1 Change in the credit spread of a speculative-grade firm in response to different size shocks in market liquidity Liquidity Shock = 2 Bond Maturity (years)

Liquidity Shock = 4

Par Bond Yield Spread (bps)

Increase in Yield Spread (bps)

Default Component (fraction of spread change)

Increase in Yield Spread (bps)

Default Component (fraction of spread change)

1

330

169.6

41.0%

523.0

42.6%

3

330

144.6

30.8%

422.1

28.9%

6

330

128.9

22.4%

369.8

18.9%

10

330

120.3

16.9%

341.9

12.3%

Table 5.2 Change in the credit spread of an investment-grade firm in response to different size shocks in market liquidity Liquidity Shock = 2 Bond Maturity (years)

Liquidity Shock = 4

Par Bond Yield Spread (bps)

Increase in Yield Spread (bps)

Default Component (fraction of spread change)

Increase in Yield Spread (bps)

Default Component (fraction of spread change)

1

100

61.7

18.8%

190.7

21.3%

3

100

57.2

12.6%

174.3

13.9%

6

100

56.4

11.3%

166.9

10.1%

10

100

53.7

6.9%

159.7

6.1%

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he numerical example shows that the change in the credit spreads of BB-rated irms are greater for a particular size shock to market liquidity than A-rated irms. Note that the increase in the credit spread for both liquidity shocks is greater for the speculative grade irm than for the investment grade irm. Furthermore, if we compare the results of the speculative and the investment grade irms for a given debt maturity, the default risk contributes a greater fraction of the credit spread increase for the BB-rated irm. his result makes intuitive sense—the speculative BB-rated irm is closer to its default boundary and is therefore more vulnerable to any increase in default boundary caused by a shock to market liquidity. his numerical example also sheds some light on the light-toquality phenomenon in the bond market whereby investor demand for high-quality bonds increases relative to lower-quality bonds ater major liquidity disruptions in inancial markets. he yield spreads (prices) of low-quality bonds increase (decrease) more than similar maturity highquality bonds. Implications for asset pricing and risk management he dual nature of default risk and market liquidity risk for corporate bonds in particular and for defaultable securities more generally has implications for investors, issuers, risk managers, and regulators. Investors should incorporate not only the cost of investments but also the cost of liquidating their bond holdings before maturity into their portfolio decisions. he frameworks we discussed will allow risk managers to incorporate the default premium and a liquidity premium in corporate bond pricing, thereby improving the accuracy and reliability of risk measures. Firms maintain debt inancing that needs to be reinanced as bonds mature. he rollover mechanism illustrates that worsening secondary market liquidity can afect a irm’s solvency due to earlier default. An interesting application of these pricing models is that it allows the quantifying of the efects of liquidity-provision policies on the corporate bond market. A market-wide liquidity provision not only reduces investors’ required compensation for bearing liquidity risk but also alleviates some default risk faced by bond investors. A better functioning inancial

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Market Liquidity Risk

market helps mitigate a irm’s rollover risk and thus relaxes its default risk.55 Market liquidity also plays a central role in the size of credit losses for debt instruments. A better understanding of the interactions between bond credit risk and market liquidity is useful for regulators as such relations allow them to adopt regulatory polices to better promote competition and eiciency.

6

Financial Regulation and Liquidity Risk Management

Introduction he celebrated author Peter Bernstein explained in his book Capital Ideas: he Improbable Origins of Modern Wall Street, “Without the stock market, the market for corporate ownership would be like the market for houses. Agents have to advertise or use some cumbersome method of inding the other side of the deal. Real estate agents earn commissions of 6% or more, while the commission on a typical stock transaction is less than 1%. Ater the house has been sold, only the principles and their close friends know what the price was.”1 he increased marketability of inancial instruments and the transferability of risks have been one of the major features of the modernization of the inancial system and our increased reliance on a liquid market for smooth operation of the inancial system. One of the major themes we developed in previous chapters is that lower market liquidity leads to lower prices of securities and therefore higher required return by investors. A more liquid market lowers the cost of capital of irms that rely on the issuance of securities such as debt and equity to raise capital. A lower cost of capital increases investment and afects corporate performance. he proper design of market structure and other arrangements of trading, discussed in chapter 3, can increase the liquidity of traded assets and reduce investors’ required returns. hese improvements also afect the value of irms, as shown in a study by professors Vivian Fang, homas Noe, and Sheri Tice, who analyzed the efects a decrease in the tick size for US securities had on the value of irms with securities afected by this change. Fang, Noe, and Tice presented

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Market Liquidity Risk

irm-level evidence that liquidity is correlated to company value. Firms with more liquid stocks, identiied by the increased market liquidity due to the smaller tick size, have higher market-to-book value ratios.2 While these types of structural changes improve market liquidity, individual irms can also take steps to ine-tune the liquidity of their securities. We discuss examples of corporate policies that irms can adopt to increase the liquidity of their claims. Firms also implement risk management and internal control systems, which are aimed to assess and monitor market liquidity risk. According to Arnaud Bervas, “excessively optimistic assessment of market liquidity, i.e. the belief that transactions can be settled at current prices without any notable delays or transaction costs, may be a serious threat to inancial stability.”3 It is crucial to address these threats to inancial stability by improving the management of market liquidity risk at the irm level. he dark side of market liquidity is the very illusion that it will always be around. In reality, market liquidity can suddenly disappear from markets, degenerating into a systemic crisis due to contagion across asset markets when the shock in one market also afects other markets. he global market disruptions during the 2008 crisis and subsequent comprehensive explanations for the mechanisms that led to the crisis, all elucidate the importance of suiciently liquid inancial markets. An illiquid market drains wealth, similar to when you are stuck in traic jam—you consume gas but go nowhere. Economists at the International Monetary Fund (IMF) found that the total amount of government recapitalizations, asset purchases, and guarantees during the period 2007 to 2011 amounted to nearly $5 trillion. his is equivalent to approximately 16 percent of the gross domestic product (GDP) of these economies, or nearly $5,000 per citizen.4 Dislocations appear when market liquidity evaporates from some key markets. For example, during the 2008 crisis, asset prices of many securities, including mortgage-backed securities (MBS) and asset-backed securities (ABS) were low or not available, relecting not only impairment of the cash low due from these securities, but also unusually high-risk and liquidity premiums. his episode illustrates the heightened signiicance of market liquidity for inancial stability. We discuss two general mechanisms

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whereby ire sales manifest. hese mechanisms provide valuable insight into spread of the systemic liquidity crisis of 2007–2008. his chapter concludes with an overview of regulatory proposals aimed at avoiding a repeat of the liquidity crisis of 2007 to 2009. he regulatory framework before the recent inancial crisis was inadequate. For example, the average risk weight of bank balance sheets, a measure of capital adequacy under Basel I and II, declined from 70 percent to below 40 percent between 1993 and 2008, but the decline did not correspond to the realities of risk in the banking industry.5 Following the severe market disruptions of the crisis, ex-ante regulation of market liquidity risk is at the heart of proposed changes to inancial regulations. An objective of the liquidity measures proposed under Basel III and some of the additional measures aimed at identifying systemically important inancial institutions are essential steps needed to avoid the pitfalls of systemic market liquidity risks. Corporate policies to enhance liquidity of issued securities Higher liquidity is associated with a lower expected return on assets. Companies thus have an incentive to invest resources in increasing the liquidity of their inancial claims in order to reduce their cost of capital. Firms can increase the liquidity of their equity claims by going public—a very popular strategy—according to data published by the Securities Industry and Financial Markets Association (SIFMA), initial public oferings raised $22.9 billion in 89 deals and an additional $55 billion on 237 secondary market oferings during the second quarter of 2014 alone.6 Going public is costly because of the direct cash outlays involved. For example, major exchanges such as the New York Stock Exchange (NYSE) or the National Association of Securities Dealers Automated Quotations (NASDAQ) require companies to pay application fees, even before going public, and additional listing fees following a successful initial public ofering. Stock exchanges also require the listed irms to maintain certain minimum standards of stockholders’ equity, and a number of shareholders among other things. Going public goes hand in hand with a greater separation between ownership and control, and greater transparency of

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Market Liquidity Risk

the company’s business policies. Public listing, however, is no guarantee that the shares will be actively traded with narrow bid-ask spreads and informative prices. To achieve this, a company can also act on other fronts.7 We discussed the importance of symmetric information for market liquidity. In an efort to reduce information asymmetries, irms can accordingly reduce the informational advantage of one group of investors (e.g., the insiders) over another, such as institutional or retail investors, by publishing inancial disclosures and occasional announcements and press releases. An example of a regular inancial disclosure created by management of irms in the United States to communicate with investors and analysts is the annual report iled pursuant to the Securities and Exchange Act of 1934, Form 10-K. Companies would engage in such disclosures voluntarily even if they were not mandatory, to improve the liquidity of their claims—how would investors purchase equity in companies they have not heard of? Companies willingly publish forecasts and other information, and voluntarily have their publicly traded bonds rated (while not doing the same for their privately placed bonds). Ratings provide investors with more information on the bond, increasing its liquidity and helping to reduce required yield and therefore the cost of capital for the irm. According to a 2014 study published in the respected he Journal of Finance, “the supply of public information [to] show that irms seek to shape their information environments through voluntary disclosure and that such eforts improve their liquidity. he former result conirms the central assumption made in theoretical models of disclosure. he latter result contributes to our understanding of liquidity in inancial markets by showing that managers can actively inluence the liquidity of their shares and, ultimately irm value.”8 Another measure being used by irms to increase the liquidity of their securities is to utilize the services of underwriters at investment banks. he role of the underwriter is to increase the liquidity of new securities by disclosing information about the issuer and alleviating investors’ apprehension about trading the new security. Ideally, the underwriter provides a credible “stamp of approval” on the quality of the company and its

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future prospects. he underwriter also provides market-making services in the irst days ater the issuance, thus ensuring the security’s liquidity during that period. It is also typical for multiple investment banks to be involved in large issuance. he largest initial public ofering (IPO) in history, Alibaba, a Chinese online and mobile commerce company, was jointly underwritten by Credit Suisse, Deutsche Bank, Goldman Sachs, J.P. Morgan, and Citigroup in September, 2014.9 Management of market liquidity risk Traders and portfolio managers rely on various risk measures tailored to the securities they trade or manage. For example, a ixed income portfolio manager concerned about interest rate risk typically calculates duration, convexity, exposure to volatility, and time decay on a periodic basis to monitor the market risks of their portfolio. While these granular measures are critical for traders, senior management and regulators ind a single measure such as value-at-risk (VaR), which summarizes the total risk of the portfolio, more useful. In particular, VaR is an estimate of the maximum loss that may be incurred on a position or portfolio at a given time horizon and a given conidence level. We explain the implications of market liquidity for risk management using VaR.10 Traditional market risk management is ignorant of market liquidity, and care exclusively about portfolio value changes under the implicit assumption of ideal, frictionless markets by using the midprice (or an equivalent of the midprice) to measure portfolio risk. In reality, traders do not realize the midprice; instead, they realize the midprice less the bidask spread. Another important facet of market liquidity is that there may be delays in buying or selling securities. Delays due to a lack of counterparties or lack of suicient market depth are prevalent particularly when large trades are needed to reposition or hedge a portfolio. Market participants further assume that the liquidation of positions will have no impact on the market and that the bid-ask spread will remain stable irrespective of the size of the position. Relying on frictionless markets when estimating risk measures therefore underestimates the true risk in inancial markets, because the realized

148

Market Liquidity Risk

value upon liquidation can deviate signiicantly from the market midprice. here are ad hoc techniques for re-evaluating VaR by artiicially increasing the volatility of positions deemed illiquid, or by lengthening the time horizon used for calculating VaR to ensure an orderly liquidation of the position. Risk managers can use ten business days instead of the more typical one-day holding period to calculate the market VaR. Liquidity-adjusted value at risk improves the assessment of market risk by allowing prices to deviation from their fundamental. Put diferently, liquidity-adjusted value at risk extends the traditional value at risk calculation to incorporate the cost of liquidity. he concept behind the liquidity-adjusted VaR is simple. Market risk is split into two components: the return risk, which can be thought of as a pure market risk component (for example, interest rate risk in a ixed income portfolio), and a liquidity risk. he developers of liquidity-adjusted VaR distinguish between exogenous liquidity risk, which is determined by the collective behavior of all market participants and is not under the control of any one participant, and endogenous liquidity risk, which is speciic to the size of a particular participant’s trading position.11 he relationship between the trade price and the size of a trade is afected by the aggregate size of all concurrent trades in the particular security. If the size of the order is smaller than the quote depth, the cost of immediate execution is half the bid-ask spread. If the size of the order exceeds the quote depth, the cost of immediate execution is greater than half the bid-ask spread. he excess over the halfspread relects the endogenous liquidity cost. Implementation is inhibited by a lack of reliable data. We present a liquidity-adjusted VaR framework that incorporates the exogenous liquidity risk. It is easy to implement and uses readily available data. Liquidity-adjusted value-at-risk We develop the framework of liquidity-adjusted VaR, assuming a single security and then extend it to a portfolio of securities at the end of the section.12

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he basic elements of the traditional VaR are the distribution of returns of the security over a predeined time period, such as one day. here are two important variables to deine VaR, the time period, usually one day, and the conidence level, usually 99 percent. he conidence level relects the degree of certainty of the potential loss. Asset returns are the log diference of midprices:  P  rt = ln Pt − ln Pt −1 = ln  t  .  Pt −1  Taking a one-day horizon over which the change in asset value is considered, and assuming that one-day returns follow a Gaussian normal distributions, the VaR at 99 percent conidence level is VaRt = Pt 1 − e −2.33σ t  , where σ t2 is the volatility of return. Without loss of generality we assumed a zero mean return. Assume that the average historical transaction cost is representative of liquidation. he liquidity-adjusted VaR is then deined as regular VaR plus the cost of liquidating positions: Liquidity VaRt = VaRt + Liquidation Cost  s + kσ s  , Liquidation Cost = Pt   2  where σs is the volatility of the relative spread, k is a scaling factor, and s is the average relative spread deined in terms of the bid and ask market prices. s=

Offer Price − Bid Price . Mid Price

Standard VaR calculations presuppose a distribution of returns. he distribution may be determined using a parametric, historical simulation, or Monte Carlo simulation. he same approaches may be applied to the distribution of bid-ask spreads. However, spread distributions are not normal distribution, and empirical results show that these could

150

Market Liquidity Risk

be multimodal. Market liquidity also exhibits signiicant variations over time characterized by high-liquidity and low-liquidity regimes. he objective of using a historical simulation would therefore be to determine the “worst-case” scenario spread for a given time horizon and conidence threshold. he highest exogenous liquidity cost is thus obtained. To treat the return risk and liquidity risk jointly, we make the conservative assumption that extreme return events and extreme spread events happen concurrently. he correlation between the midprice movements and spreads is not perfect, but it is nevertheless strong enough during extreme market conditions to enable and encourage us to view return risk and exogenous liquidity risk as experiencing extreme movements simultaneously.  s + kσ s  Liquidity VaRt = Pt 1 − e −2.33σ t  + Pt  .  2  he factor k represents a “correction factor” that can be used to distinguish mathematically between the return distributions in a normal market and in a stressed market. A factor of k = 1 represents a normal distribution, and k > 1 represents a deviation from normal, which is typical in a stressed market. Large samples of daily bid-ask spreads on all securities may not be readily available. An alternative measure of market liquidity is the Roll measure, discussed in chapter 4, which seeks to provide an estimate of the implied spread using only observed market prices. Using historical simulation to estimate VaR proceeds by using the historical distribution of returns and the historical distribution of bid-ask spreads or Roll coeficients over the same period to estimate the distribution of possible losses—including liquidity cost—on a current position.13 In the traditional portfolio VaR, the covariance matrix of asset returns is the key bridge from a single-instrument measurement to portfolio risk. A liquidity-adjusted portfolio VaR could proceed in a similar fashion, but it would require an assumption of multivariate normality of spreads and the estimation of a spread covariance matrix. But spread distributions are not nearly as well behaved as return distributions, and a feasible

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alternative would be to calculate a portfolio-level bid-ask spread series by taking a weighted average of the individual bids and asks and then using the instrument-level liquidity-adjusted portfolio VaR to adjust the standard portfolio VaR for exogenous liquidity risk. Limitation of liquidity-adjusted value-at-risk Members of the Basel Committee on Banking Supervision pointed out that “the liquidity of traded products can vary substantially over time and in unpredictable ways,” and moreover, “studies suggest that banks’ exposures to market risk and credit risk vary with liquidity conditions in the market.”14 he former statement suggests a stochastic description of the time horizon over which a portfolio can be liquidated, and the latter highlights a dependence between credit risk and market liquidity risk. A framework with a random holding period is discussed in the context of a single portfolio by academic researchers Damiano Brigo and Claudio Nordio.15 We discussed the dependence between credit risk and market liquidity risk in chapter 5. here are two main limitations of VaR in general and liquidity-adjusted VaR in particular. VaR has limited, if any, predictive power—the popular historical VaR framework is “backward looking,” implicitly assuming that history repeats itself. he second limitation arises when all industry participants are subjected to a similar risk measure. As explained by the empirical research of Lasse Pedersen of New York University,16 “subjecting traders to liquidity-adjusted value at risk gives rise to a multiplier efect,” which causes a feedback between market liquidity and risk management. he multiplier efect discussed by Pedersen can be explained using anecdotal evidence on the inancial market crises that followed the default of Russian bonds in August 1998. Many traders lost money and, simultaneously, market volatility increased, which caused the VaR measures at many investment banks and other institutions to increase. Traders were forced to liquidate positions to comply with risk limits, which led to falling prices and lower market liquidity that further exacerbated riskmanagement problems.

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Market Liquidity Risk

Mechanisms of systemic liquidity risk

he lack of market liquidity under stress is generally a symptom of problems that originate elsewhere. Market liquidity ultimately depends on the way in which market participants perceive and respond to risks and returns. When funding liquidity and market liquidity chase each other deeper and deeper into an abyss, it can prolong and spread a liquidity crisis into a systemic event. A well-researched case in point is the 2007–2008 crisis, which originated in a relatively the small market segment for subprime securities but was catapulted into a global systemic liquidity crisis. he term “ire sale”—adapted from the sale of ire-damaged goods at discounted prices and dating back to the nineteenth century—became a popular nomenclature among academics and other observers who developed theories that shed light on the development and spread of the crisis. A ire sale is basically a sale of securities mandated by the fact that, without the sale, the irm has no other means of obtaining additional funding or paying existing creditors. A forced sale of securities exerts signiicant downward pressure on prices away from fundamentals that cause signiicant losses to sellers.17 When a ire sale leads to a sharp reduction in an asset’s price, similar assets held by other market participants decline in value as well, which might bring them into inancial distress and force asset sales. his self-reinforcing process can lead to a downward spiral in asset prices and the net worth of market participants. Fire sales cause direct losses, but the negative externality of ire sales can result in substantial second-round spillover losses. According to a study by economists at the Federal Reserve Bank of New York, a moderate one percent shock to assets measured during a relatively stable market conditions during August 2013 produced ire-sale spillover losses of 23 percent of system capital for broker-dealers and 19 percent of system capital for commercial banks. Fire sale spillover losses during the inancial crisis were between two to three times larger.18 he other side of market illiquidity in general and ire sales in particular is funding illiquidity—these are essentially two sides of the same coin—a shock to one or the other can cause a negative spiral that leads to systemic liquidity risk.19 Professors Markus Brunnermeier and Lasse

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Pedersen looked at the correlation between the increase in margins and the occurrence of liquidity crises. heir results illustrate that the margin increased around each of the liquidity crises of 1987, 1990, 1998, and 2007. Most recently, margins across most asset classes increased signiicantly during the summer of 2007 (we discussed this in more detail in chapter 3). Such margin increases are “destabilizing,” in that their high levels force speculators to delever their positions in times of crisis.20 Stress in the funding of market participants can develop due to an inability to raise cash, either through debt or equity inancing, or due to large outlows and cash withdrawals from external investors. he liquidity crisis of 2007–2008 developed because key market participants such as broker/dealer intermediaries, some hedge funds, and commercial banks experienced one of the two forms of funding illiquidity that led to correlated ire sales among market participants.21 he irst mechanism whereby ire sales occur is when irms are unable to raise debt inancing due to leverage constraints. he theory has two components: (a) the amount of debt inancing available to an intermediary is proportional to the equity capital of the intermediary times a leverage multiple, which is set by the lenders and (b) the demand for assets is a function of the total funds (debt plus equity) available to the intermediary. his mechanism is at work at irms such as hedge funds and brokers/ dealers that inance security purchases using the repurchase market. he amount of funding, or leverage, available through security repurchases depends on the margin, or haircut, on the security. he haircut is also afected by the market liquidity of the repurchased security. In the event that the security is less liquidity, the margin will increase proportionally, which lowers the amount of leverage available through repurchasing the security. his leads to a decreased demand for the asset as collateral in a repurchase agreement, which decreases the liquidity of the security even further. he increase in margins on structured securities such as ABS and MBS in 2007–2008 decreased the available leverage, which in turn decreased the demand for assets. For some structured securities, no prices were available—an extreme example of an illiquid market. Another example of an escalating liquidity spiral developed when the hedge fund Long Term Capital Management (LTCM) could ultimately not fund its

154

Market Liquidity Risk

positions and was taken over by 14 banks in September 1998. he demise of the hedge fund Amaranth is a more recent example in which the amount of risk taking in the fund’s portfolio far exceeded the amount of available capital. In September 2006, Amaranth lost 65 percent of its $9.2 billion assets. he fund’s assets were subsequently transferred to JP Morgan Chase and Citadel Investment Group.22 he second mechanism whereby ire sales can occur is when the equity risk capital of irms is constrained and they are unable to raise additional equity, but face no constraints in raising debt inancing. Because the irm has limited equity capital, its management becomes more risk averse. his leads to a disproportionate demand for low-risk assets and a reduced demand for risky assets in states of the world in which the probability of distress of the irm is high. When many irms are close to distress, the demand for risky assets across all irms is low, causing asset prices to fall for those securities. his mechanism is also at work when losses erode capital levels at banks. Since regulatory capital requirements at commercial banks penalize holdings of risky assets in favor of riskless assets, banks respond by shiting their portfolios to favor riskless assets. Banks accordingly require a higher risk premium to purchase risky assets, causing asset prices to fall. Feasibility of regulating systemic liquidity risk he two mechanisms we discussed in the prior section highlight several important vulnerabilities that can lead to a systemic liquidity crisis: market liquidity is integrally linked to funding liquidity, and not all securities are equally liquid. Securities have market liquidity proiles— the liquidity of securities also changes with general market conditions.23 Market participants need to be aware of the liquidity proiles of their assets relative to their liabilities. Participants should strive to match the market liquidity proile of assets with the market liquidity proile of their funding.24 Another insight provided by the theory of ire sales is that the liquidity proile of securities depends not only on the type of product and inherent

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risks but also on who holds the security. Market participants have diferent funding structures as well as diferent regulatory requirements that drive their corporate strategies and expose their asset holdings to diferent risks. Developing information about incipient market vulnerabilities is very challenging. By the end of 2014, regulators were still grappling with the questions about the types and feasibility of such information. he ex-ante regulation of liquidity risk at individual institutions is an attempt to prevent an excessive buildup of exposure to liquidity risk. In the next section, we discuss two of the liquidity proposals being reviewed as part of the Basel III regulatory proposals. One of these regulations, the liquidity coverage ratio (LCR), is designed, among other things, to encourage banks to draw down their high-quality liquid assets as opposed to getting rid of illiquid assets at ire sale prices. Other initiatives involve the development of systemic risk measures that aggregate the risk information across individual institutions. Such measures can be used to monitor the commonality of exposures and their interactions. he complexity and size of the inancial system calls for a diversity of legal and institutional constraints and market practices. here is a corresponding diversity of proposals that emphasizes diferent aspects of systemic risk. As of 2012, there were as many as 30 diferent proposals ranging from macroeconomic measures looking at property price and credit-gap indicators to market micro-structure measures of market liquidity.25 Another active area of regulatory development is the designation of some inancial institutions as systemically important inancial institutions (SIFIs). Under the Dodd-Frank Act, a inancial irm is designated as a SIFI if it “holds assets that, if liquidated quickly, would cause a fall in asset prices and thereby . . . cause signiicant losses or funding problems for other irms with similar holdings”.26 Liquidity regulations under Basel III he central bank governors of the Group of Ten (G10) countries27 established the Committee on Banking Regulations and Supervisory Practices

156

Market Liquidity Risk

at the end of 1974 in response to disruptions in the inancial markets, most notably the market turmoil following the breakdown of the Bretton Woods system of managed exchange rates in 1973. he Committee on Banking Regulations and Supervisory Practices was later renamed the Basel Committee on Banking Supervision. It was designed as a forum for regular cooperation between its member countries on banking and supervisory matters. he Committee exists to enhance inancial stability by improving supervisory know-how and the quality of banking supervision worldwide.28 he Basel Committee typically focused on regulations to ensure that banks have suicient capital for the amount of risk they were taking. Before 2008, there were no global liquidity regulations for banks. Nothing prevented banks from relying heavily on short-term markets to inance highly illiquid long-term assets. he events of the 2007–2008 inancial crisis forced market participants and the Basel Committee to focus on market liquidity risks and to develop regulations that will prevent the detrimental efects of an illiquid market. In December 2010, the Basel Committee published Basel III, which was in many respects a major overhaul of bank regulations. Basel III represents the irst time that liquidity risk has been set at the global level. In particular, Basel III includes a series of rules concerned with increasing the amount of capital that banks have to keep for credit risk, tightening the deinition of capital, regulating the inancial leverage (or the amount of debt inancing relative to equity inancing), and regulating counterparty credit risk management. he novel feature of Basel III is that it includes speciic guidelines on liquidity risk that need to be met by banks. he two liquidity regulations are the LCR and the Net Stable Funding Ratio (NSFR), which aim to prevent bank insolvency as a result of liquidity pressures. To ensure that banks can implement the new regulations without disruption to their activities, Basel III is being phased in over a long period of time, starting in 2015 and ending on December 31, 2019. To provide insight into the thinking behind the liquidity regulations, consider the following example of how funding of liquidity risk can arise at a bank.29 Assume that a bank relies on short-term funding to inance longer-term assets or investments. his

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strategy exposes the bank to liquidity risk in the event that the market becomes distressed or market participants perceive the bank to have problems. Suppose that a bank uses 90-day commercial paper to fund its activities. When one 90-day issue of commercial paper matures, the bank reinances it with a new issue. he bank will continue to roll over this short-term funding every 90 days to inance continuing operations. However, in the event that the bank experiences inancial diiculties—or perceived diiculties—it is liable to become impossible for the bank to roll over its commercial paper because counterparties may not be willing to extent credit to a distressed institution. his type of problem led to the demise of Northern Rock in the United Kingdom and Lehman Brothers in the United States.

The liquidity coverage ratio30 he LCR focuses on a bank’s ability to survive a 30-day period of liquidity disruptions. he ratio assumes a complete drawdown of interbank deposits and all other short-term inancial instruments of less than onemonth maturity. he 30-day period considered in the calculation of this ratio is one of acute stress (as severe as that seen in the 2007–2008 inancial crisis) involving a downgrade of the bank’s debt by three notches, a partial loss of deposits, a complete loss of wholesale funding, increased haircuts on secured funding (so the instruments posted as collateral are not valued as highly), and drawdowns on lines of credit. he ratio is deined as Liquidity Coverage Ratio =

High Quality Liquid Assets . Net Cassh Outflows in a 30 Day Period

he Basel III regulations require the ratio to be greater than 100 percent so that the bank’s liquid assets are suicient to survive these pressures. he development of this ratio acknowledges that securities have different market liquidity and credit characteristics. High-quality liquid assets are mostly government bonds and cash, and a deined maximum percentage of mortgages and corporate bonds may be of certain lower quality.

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Market Liquidity Risk

The net stable funding ratio he NSFR aims to encourage more medium- and long-term funding of the assets and activities of banks, including of-balance sheet exposures as well as capital market activities, and thereby reduce the extent of maturity mismatch at the bank. In theory, this would lower a bank’s probability of liquidity runs and associated default. It is intended to support the institution as a going concern for at least one year if it is subject to irm-speciic funding stress. he NSFR focuses on liquidity management over a period of one year. It is deined as Net Stable Funding Ratio =

Amount of Stable Funding . Required Amount of Stable Funding

he numerator is calculated by multiplying each category of funding (capital, wholesale deposits, retail deposits, etc.) by an available stable funding factor (ASF) relecting their stability. As shown in Table 6.1, the ASF for wholesale deposits is less than that for retail deposits, which is in turn less than for Tier 1 and Tier 2 capital. he denominator is calculated from assets and of-balance sheet items requiring funding. Each of these categories is multiplied by a required

Table 6.1

Available stable funding factors for net stable funding ratio

Factor

Category

100%

Tier 1 and Tier 2 capital. Preferred stock and borrowing with a maturity greater than one year

90%

Stable demand deposits and term deposits with remaining maturity less than one year provided by retail or small business customers

80%

Less stable demand deposits and term deposits with remaining maturity less than one year provided by retail or small business customers

50%

Wholesale demand deposits and term deposits with remaining maturity less than one year provided by nonfinancial corporates, sovereigns, central banks, multilateral developments banks, and public sector entities

0%

All other liabilities and equity categories

Source: John. C. Hull, Risk Management and Financial Institutions, third edition, 2012, John Wiley & Sons, Inc.

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Table 6.2

Required stable factors for net stable funding ratio

Factor

Category

0%

Cash; Short-term instruments, securities, loans to financial entities if they have a residual maturity of less than one year

5%

Marketable securities with a residual maturity greater than one year if they are claims on sovereign governments or similar bodies with a 0% risk weight

20%

Corporate bonds with a rating of AA- or higher and a residual maturity greater than one year; Claims on sovereign governments or similar bodies with a risk weight of 20%

50%

Gold; Equity securities; Bonds rated A+ to A-

65%

Residential mortgages

85%

Loans to retail and small business customers with a remaining maturity less than one year

100%

All other assets

Source: John. C. Hull, Risk Management and Financial Institutions, Third Edition, 2012, John Wiley & Sons, Inc.

stable funding factor to relect the permanence of the funding required. Some of the applicable factors are indicated in Table 6.2. Basel III requires the NSFR to be greater than 100 percent so that the calculated amount of stable funding is greater than the calculated required amount of stable funding. Consider the following example of a bank balance sheet, shown in Table 6.3. Amount of Stable Funding = Retail Depossits × 90% + Wholesale Deposits × 50% + Tier 2 Capital × 100% + Tier 1 Capital × 100% Required Amount of Stable Funding = Cassh × 0% + Goverment Securities × 5% + Residential Mortgages × 65% + Business Loans × 85% + Other Assets ×100% he NSFR is 95 percent, which is less than the required 100 percent under Basel III. he bank therefore does not satisfy the NSFR. he new rules are tough and have the potential to dramatically change bank balance sheets. However, there is a transition period during which

160

Market Liquidity Risk Table 6.3

Example of a bank balance sheet

Assets

Liabilities

Cash

5

Retail Deposits

40

Treasury Bonds

5

Wholesale Deposits

48

Residential Mortgages

30

Tier 2 Capital

4

Small Business Loans

25

Tier 1 Capital

8

Fixed Assets

35

Total Assets

100

Total Liabilities

100

the efect of the rules will be monitored. It is possible that the rules will be eased somewhat before they are inally implemented. he core objective of the Basel III rules is to encourage banks to hold higher liquidity bufers and to lower mismatches between assets and liabilities. he debate on whether the new rules will lower the probability that any individual institution will run into liquidity problems is still ongoing. Basel III rules are also designed to prevent risks at individual banks, and are not intended or designed to mitigate systemic liquidity risks, in which the interactions of inancial institutions can result in the simultaneous inability of institutions to access suicient market liquidity and funding liquidity under stress.31 Unless the liquidity requirements are set at an extremely high level for all institutions, resulting in a prohibitive cost to the real economy, the possibility always exists that a systemic liquidity event will exhaust all available liquidity. In all circumstances, central bank support is warranted to assume that systemic liquidity shortfalls do not morph into large-scale solvency problems and undermine inancial intermediation and the real economy. hese indicators are not forward looking. More regulation or less regulation—where do we go from here? Many new regulations are designed to protect the inancial system and the broader economy against the types of market stress following the demise of Lehman Brothers and Bear Stearns in 2008. he banking industry in particular has raised concerns about the costs of new

Financial Regulation

161

regulation and warned that the new regulatory requirements may constrain banks’ ability to lend or invest, resulting in a drag on both bank earnings and broader economic growth.32 According to Bill Nelson, a Federal Reserve economist, the rule “will to some extent make credit a bit more costly, but weighed against that is that these regulations will cause inancial crises to be less likely and less frequent and less severe.”33 More stringent liquidity regulation can reduce the risk of systemic crisis, but there has been a vigorous debate about the negative impact of such regulation due to the implications this has for other aspects of the market. Two areas of concern are the market liquidity for certain overthe-counter (OTC)-traded securities and banks’ response, along other dimensions such as the size and composition of their balance sheets. An interesting case in point is the efects of reduced dealer inventories of corporate bonds. During 2012, the dealer inventories of corporate bonds failed to comply with new capital regulations that were causing increased liquidity premiums in the corporate bond with essentially no change to the fundamental cash lows of bonds.34 Proponents of ater-the-fact intervention, such as the central bank’s acting as a lender of last resort, argue that regulation is needed to ensure suicient capital ex-ante, and that market liquidity problems can be dealt with ater the fact. his argument is plausible if the lender of last resort provides liquidity assistance to a fundamentally solvent institution. Walter Bagehot succinctly captured the role of a lender of last resort as follows “In times of crisis, the central bank should lend freely (and at a penalty rate) to banks, provided that the banks are solvent and the loans are adequately collateralized.”35 However, as discussed in chapter 5, and as is clear from the experience of the 2008 crisis, credit risk and the probability of default and liquidity risk are not independent. One might argue that the brokerdealer that experiences a liquidity crunch must have some probability of having solvency problems; otherwise, they would have been able to attract short-term funding from the private market.36 hese types of arguments call for ex ante liquidity regulation as part of improving inancial stability. he debate on these issues is ongoing and will be for some time until policymakers and market participants strikes the right balance. he

162

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recent crisis revealed the limitations of classical inance theory and inadequate market regulations. However, we should continue to assess the contributions of academics and regulatory bodies, and not make hasty pronouncements like the author of this of statement from 1876: “this telephone has too many shortcomings to be seriously considered as a means of communication. he device is inherently of no value to us.”37

Notes 1

Musings on Liquidity

1. John Kay, 2013, “A Fixation on Liquidity Is Not Healthy for Financial Markets,” Financial Times, September 17, http://www.t.com/intl/cms /s/0/5e70c3b8-1f83-11e3-aa36-00144feab7de.html#axzz3Xs4Fn1oD. 2. Speech of Ben S. Bernanke, 2008, “Liquidity Provision by the Federal Reserve,” at the Federal Reserve Bank of Atlanta Financial Markets Conference, May 13, Sea Island, GA. 3. he Economist, 2010, “What Caused the Flash Crash? One Big, Bad Trade,” Joint Report of the Securities and Exchange Commission and the Commodity Futures Trading Commission, October 1. 4. Gillian Tett, 2014, “In Parched Bond Markets, Sparks Are Dangerous,” Financial Times, November 27. 5. A drachma is an ancient Greek coin. 6. K. Polanyi, C. Arensberg, and H. Pearson, eds. 1971, Trade and Markets in the Early Empires, Chicago: Regnery. 7. he irst years of the Roman Empire, between 27 BCE and 284 AD, are referred to as the principate. 8. K. Roberts, 2011, he Origins of Business, Money, and Markets, New York: Columbia University Press. 9. K. Roberts, 2011, he Origins of Business, Money, and Markets, New York: Columbia University Press 10. John Maynard Keynes, 1936, General heory of Employment, Interest and Money, New York: Palgrave Macmillan. 11. Hernando De Soto, 2000, he Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else, New York: Basic Books. 12. John Maynard Keynes, 1936, General heory of Employment, Interest and Money, New York: Palgrave Macmillan. 13. Incidentally, Keynes ofered a similar argument in 1936, some 30 years earlier.

164

Notes

14. John C. Bogle, 2011, “How the Index Fund Was Born,” Wall Street Journal, September 3. 15. he American Law Register identiies the origin of the modern system of mortgaging real property in early Jewish sacred writings such as the Talmud. he modern form of the “mortgage” is unique and has roots in American independence and the revolutionary government when “the irst legitimate commercial bank” was founded in 1781. See http://www.randomhistory.com/1-50/037mortgage.html. 16. K. J. Arrow and G. Debreu, 1954, “Existence of an Equilibrium for a Competitive Economy,” Econometrica, 22, pp. 265–290. 17. D. Diamond, 1984, “Financial Intermediation and Delegated Monitoring,” Review of Economic Studies, Vol. 51, pp. 393–414. 18. R. Levine, 1997, “Financial Development and Economic Growth: Views and Agenda,” Journal of Economic Literature, Vol. 35, pp. 688–726. 19. G. Gorton and G. Pennacchi, 1990, “Financial Intermediaries and Liquidity Creation,” he Journal of Finance, Vol. 45, No. 1, pp. 49–71. 20. H. Demsetz, 1968, “he Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53. 21. R. C. Merton, 1987, “A Simple Model of Capital Market Equilibrium with Incomplete Information,” he Journal of Finance, Vol. 42, No. 3, pp. 483–510. 22. Walter Bagehot, 1971, “he Only Game in Town,” Financial Analysts Journal, Vol. 27, No. 2, pp. 12–14, 22. 23. A. Bervas, 2006, “Market Liquidity and Its Incorporation into Risk Management,” Banc de France, Financial Stability Review, No. 8, pp. 63–79. 24. S. F. Grossman and M. H. Miller, 1988, “Liquidity and Market Structure,” he Journal of Finance, Vol. 43, No. 3, pp. 617–633. 25. F. Black, 1971, “Toward a Fully Automated Exchange, Part I,” Financial Analyst Journal, Vol. 27, No. 4, pp. 28–35. 26. W. Sharpe and G. Alexander, 1990, Investments (4th ed.), Englewood Clifs, NJ: Prentice Hall. 27. A. Shleifer and R. W. Vishny, 1997, “he Limits to Arbitrage,” he Journal of Finance, Vol. 52, No. 1, pp. 35–55.

Notes

165

28. his section closely follows the discussion in J. H. Cochrane and C. L Culp, 2003, “Equilibrium Asset Pricing and Discount Factors: Overview and Implications for Derivatives Valuation and Risk Management,” in Modern Risk Management: A History, P. Field, ed. London: Risk Books, pp. 57–92. 29. W. Sharpe, 1964, “Capital Asset Prices: A heory of Market Equilibrium under Conditions of Risk,” he Journal of Finance, Vol. 19, pp. 425–442. 30. F. Black, 1972, “Capital Market Equilibrium with Restricted Borrowing,” Journal of Business, Vol. 45, pp. 444–455. 31. Y. Amihud, A. Hameed, W. Kang, and H. Zhang, 2013, “he Illiquidity Premium: International Evidence,” Working Paper.

2

Financial Crises and Liquidity Traffic Jams

1. John Maynard Keynes, 1936, General heory of Employment, Interest and Money, San Diego, New York and London: Harcourt. 2. M. K. Brunnermeier, 2009, “Deciphering the Liquidity and Credit Crunch 2007–2008,” Journal of Economic Perspectives, Vol. 23, pp. 77–100; and G. Gorton, 2009, “Information Liquidity and the (Ongoing) Panic of 2007,” American Economic Review: Papers & Proceedings, Vol. 99, No. 2, pp. 567–572. 3. G. Gorton, 2009, “Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13. 4. J. Coval, J. Jurek, and E. Staford, 2009, “he Economics of Structured Finance,” Journal of Economic Perspectives Vol. 23, No. 1, pp. 3–25. 5. Marcia Stigum and Anthony Crescenzi, 2007, Stigum’s Money Market (4th ed.), New York: McGraw-Hill. 6. Patrick E. McCabe, September 2012, “he Cross Section of Money Market Mutual Fund Risks and Financial Crisis,” Federal Reserve Board, Divisions of Research & Statistics and Monetary Afairs, Finance and Economics Discussion Series Working Paper No. 2010–51.

166

Notes

7. B. Duygan-Bump, P. M. Parkinson, E. S. Rosengren, G. A. Suarez, and P. S. Willen, 2010, “How Efective Were the Federal Reserve Emergency Liquidity Facilities? Evidence from the Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility,” Working Paper QAU10–3, Federal Reserve Bank of Boston, April 29. 8. US Securities and Exchange Commission, 2010, “Money Market Fund Reform: Final Rule,” available at www.sec.gov/rules/inal/2010/ ic-29132.pdf, February 23. Release no. IC-29132. 9. US Securities and Exchange Commission, 2010 at 10075, Securities and Exchange Commission, Federal Register, Vol. 75, No. 42, hursday, March 4, Rules and Regulations. 10. F. A. Longstaf, 2004, “he Flight-to-Liquidity Premium in U.S. Treasury Bond Prices,” Journal of Business, Vol. 77, No. 3, pp. 511–526. 11. O. Vasicek, 1977, “An Equilibrium Characterization of the Term Structure,” Journal of Financial Economics, Vol. 5, pp. 177–188. 12. M. K. Brunnermeier and M. Yogo, February 2009, “A Note on Liquidity Risk Management,” NBER Working Paper No. 14727. 13. Bengt Holmstrom and Jean Tirole, 2011, Inside or Outside Liquidity, Cambridge: MIT Press. 14. P. Pozsar, 2013, Institutional Cash Pools and the Triin Dilemma of the U.S. Banking System. NYU Stern, Financial Markets, Institutions & Instruments: Topics in Financial Intermediation, New York: New York University Salomon Center and Wiley Periodicals. 15. Bengt Holmstrom and Jean Tirole, 2011, Inside or Outside Liquidity, Cambridge: MIT Press. 16. D. W. Diamond and P. H. Dybvig, 1983, “Bank Runs, Deposit Insurance, and Liquidity,” Journal of Political Economy, Vol. 91, No. 3, pp. 401–419. 17. M. K. Brunnermeier, 2009, “Deciphering the Liquidity and Credit Crunch 2007–2008,” Journal of Economic Perspectives, Vol. 23, pp. 77–100. 18. D. Covitz, N. Liang, and G. A. Suarez, 2013, “he Evolution of a Financial Crisis: Collapse of the Asset-Backed Commercial Paper Market,” he Journal of Finance, Vol. 68, No. 3, pp. 815–848.

Notes

167

19. G. Gorton and A. Metrick, 2012, “Securitized Banking and the Run on Repo,” Journal of Financial Economics Vol. 104, pp. 425–451. 20. Gorton and Metrick calculated the repo haircut on a basket of securities comprised of an equally weighted portfolio of student loan, credit card, and auto loan ABS, residential mortgage backed securities, commercial mortgage backed securities, subprime mortgages, collateralized loan obligations and collateralized debt obligations and corporate securities. 21. Gorton referred to this dynamic as the irst “run on repo.” 22. G. Gorton, 2009, “Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13. 23. European Central Bank Working Paper Series No 1126, December 2009. 24. D. W. Diamond and R. G. Rajan, 2011, “Fear of Fire Sales, Illiquidity Seeking and Credit Freezes,” Quarterly Journal of Economics, Vol. 126, Issue 2, pp. 557–591. 25. M. Mitchell, L. H. Pedersen and T. Pulvino, 2007, “Slow Moving Capital,” AEA Papers and Proceedings, pp. 215–220. 26. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” he Review of Financial Studies, Vol. 24, No. 6, pp. 1980–2022. 27. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” he Society for Financial Studies, Vol. 22, no. 6, pp. 2202–2238. 28. R. C. Merton, 1987, “A Simple Model of Capital Market Equilibrium with Incomplete Information,” he Journal of Finance, Vol. 43, No. 3, pp. 483–510. 29. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, No. 2, pp. 373–410. 30. F. A. Longstaf, 2009, “Portfolio Claustrophobia: Asset Pricing in Markets with Illiquid Assets,” American Economic Review, Vol. 99, pp. 1119–1144. 31. B. S. Bernanke, 2007, “he Recent Financial Turmoil and Its Economic and Policy Consequences,” October 15, Economic Club of New York,

168

Notes

New York, http://www.federalreserve.gov/newsevents/speech/bernanke20071015a.htm. 32. L. Fisher, 1959, “Determinants of Risk Premiums on Corporate Bonds,” Journal of Political Economy, Vol. 67, No. 3, pp. 217–237. 33. Stéphane Loisel, 2012, “From Liquidity Crisis to Correlation Crisis, and the Need for ‘Quanls’ in ERM,” Risk Management, Issue 25, pp. 16–18. 34. V. Acharya and S. Schaefer, 2006, “Liquidity Risk and Correlation Risk: Implications for Risk Management,” September, Working Paper.

3

Market Structures and Institutional Arrangements of Trading

1. We are interchangeably using the terms “dealer” and “market maker” to mean the entity or market participant fulilling the role of inancial intermediation. 2. A deinition of “all available information” is information about historical security prices, public information such as earnings announcements, stock splits, etc., and private information relevant for security prices. he reader interested in exploring deinitions of “all available information” is referred to work by 2013 Nobel Prize winner Eugene Fama. 3. E. F. Fama, 1970, “Eicient Capital Markets: A Review of heory and Empirical Work,” he Journal of Finance, Vol. 25, No. 2, pp. 383–417. 4. E. F. Fama, 1970, “Eicient Capital Markets: A Review of heory and Empirical Work,” he Journal of Finance, Vol. 25, No. 2, pp. 383–417. 5. H. Demsetz, 1968, “he Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53. 6. H. Demsetz, 1968, “he Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53. 7. M. O’Hara, 2001, “Overview: Market Structure Issues in Market Liquidity,” BIS Papers, Market Liquidity: Proceedings of a Workshop held at the Bank of International Settlements, No. 2.

Notes

169

8. D. Easley and M. O’Hara, 1987, “Prices, Trade Size and Information in Securities Markets,” Journal of Financial Economics, Vol. 19, pp. 69–90. 9. A. S. Kyle, 1985, “Continuous Auctions and Insider Trading,” Econometrica, Vol. 53, No. 6, pp. 1315–1335. 10. If trading were solely information-based, uninformed traders would do better to leave the market rather than face a certain loss when trading with informed traders. In this discussion, we assume that uninformed trade occurs due to exogenous demand, such as an imbalance in the timing of consumption and income or from portfolio considerations. 11. A. Akerlof, 1970, “he Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics, Vol. 84, No. 3, pp. 488–500. 12. Another popular measure of liquidity is trading volume, but many consider this to be a lawed indicator. High trading volume does not necessarily imply high liquidity, as was made painfully clear during the “lash crash” on May 6, 2010, when the Dow Jones Industrial Average experienced its largest one-day decline of 988.5 points (about 9%) and the second-largest point swing in its history. For a few minutes, $1 trillion in market value vanished. he SEC observed that, “especially in times of signiicant volatility, high trading volume is not necessarily a reliable indicator of market liquidity.” See Findings Regarding the Market Events of May 6, 2010, September 30, 2010, Report of the stafs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues. 13. L. R. Glosten and P. R. Milgrom, 1985, “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Trades,” Journal of Financial Economics, Vol. 14, pp. 71–100. 14. he asymmetric information model is also an important hypothesis of how information in the order low becomes impounded in prices, as discussed in the section titled “Market microstructure insights into security price formation.” 15. he “trade-through rule” is absent from regulation MiFID in the European Union. 16. P. Hofman, March 2013, “Adverse Selection, Market Access and Inter-Market Competition,” European Central Bank, Working Paper, No. 1519.

170

Notes

17. his example closely follows an example discussed in T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: heory, Evidence, and Policy, New York: Oxford University Press. 18. B. Biais, L. Glosten, and C. Spatt, 2004, “Market Microstructure of Stock Markets, Working Paper. 19. D. Duie, 2012, Dark Markets: Asset Pricing and Information Transmission in Over-the-Counter Markets, Princeton Lectures in Finance, Princeton, NJ: Princeton University Press. 20. Tabb Report, September 2012, “he New Global Risk Transfer Market: Transformation and the Status Quo,” Tabb Group, V10: 033. 21. Securities and Exchange Commission, 1998, “Regulation of Exchanges and Alternative Trading Systems,” Release No. 34–40760; File No. S7–12–98. 22. he percentage is based on the average daily volume of shares traded during November 2014. 23. Regulation NMS, Exchange Act Release No. 51808, June 2005. Similar legislation in Europe, the Markets in Financial Instruments Directive (“MiFID”), was implemented in November 2007. 24. he four main features of Regulation NMS are the Order Protection Rule, the Access Rule, the Sub-Penny Rule, and the Market Data Rule. 25. BATS Global Markets, http://www.batstrading.com/market_summary/. 26. Detailed posttrade information on US corporate bonds is available through the Trade Reporting and Compliance Engine (TRACE), which provides the price of the transaction within minutes of the trade. For trade sizes less than a stipulated quantity, trade sizes are also reported. 27. Y. Amihud and H. Mendelson, 1980, “Dealership Markets: Market Making with Inventory,” Journal of Financial Economics, Vol. 8, pp. 31–53. 28. D. Duie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847. 29. D. Duie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847.

Notes

171

30. D. Duie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847. he rest of this section closely resembles the discussion in Duie et al. 31. his dynamic is similar to the trade-of between price and immediacy that exists in limit orders versus market orders in order-driven markets, which is discussed in the section titled “Key structural features of limit order books. 32. M. O’Hara, 2003, Market Microstructure heory, Malden, MA; Oxford, UK; Victoria, Australia: Blackwell Publishing. 33. B. Biais, L. Glosten, and C. Spatt, 2005, “Market Microstructure: A Survey of Micro-foundations, Empirical Results and Policy Implications, Journal of Financial Markets, Vol. 8, pp. 217–264. 34. T. Foucault, 1999, “Order Flow Composition and Trading Costs in a Dynamic Limit Order Market,” Journal of Financial Markets, Vol. 2, pp. 99–134. 35. CME Group, CME Globex Reference Guide. 36. H. Demsetz, 1968, “he Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53. 37. E. Portniaguina, D. Bernhardt, and E. Hughson, 2006, “Hybrid Markets, Tick Size and Investor Trading Costs,” Journal of Financial Markets Vol. 9, pp. 433–447. 38. L. Glosten, 1994, “Is the Electronic Open Limit Order Book Inevitable?” he Journal of Finance, Vol. 49, pp. 1127–1161. 39. Market participants are considering proposals about alternatives, for example, the implementation of frequent batch auctions proposed by Budish et al. See E. Budish, P. Cramton, and J. Shim, 2015, “he High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response,” Working Paper. 40. R. Huang, and H. Stoll, 1997, “he Components of the Bid-Ask Spread: A General Approach,” Review of Financial Studies, Vol. 10, pp. 1035–1064. 41. C. Parlour, and D. Seppi, 2003, “Liquidity-Based Competition for Order Flow,” Review of Financial Studies, Vol. 16, pp. 301–343. 42. H. Demsetz, 1968, “he Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53.

172

Notes

43. H. R. Stoll, August, 2000, “Friction,” he Journal of Finance, Volume 55, No. 4, pp. 1478–1514. 44. E. R. Sirri, 2008, Keynote Speech at the SIFMA 2008 Dark Pools Symposium, New York. 45. E. R. Sirri, 2008, Keynote Speech at the SIFMA 2008 Dark Pools Symposium, New York. 46. H. Zhu, 2013, “Do Dark Pools Harm Price Discovery?” Forthcoming, Review of Financial Studies. 47. he US Securities and Exchange Commission 2010 Concept Release on Equity Market Structure. See Exchange Act Release No. 61358 (January 13, 2010); Exchange Act Release No. 61908 (April 14, 2010). 48. Securities and Exchange Commission, 2010, Regulation NMS, 17 C.F.R. Parts 200, 201, 230, 240, 249 and 270, Release No. 34–51808, File No. S7–10–04, RUN 3235-AJ18 (“Reg NMS”) at 21 and 22. 49. L. Melamed, 2015, “he Day the Shouting Stopped,” he Wall Street Journal, February 11, 2015. 50. D. Easley, M. L. de Prado, and M. O’Hara, eds., 2013, High-Frequency Trading: New Realities for Traders, Markets and Regulators, London: Risk Books. 51. D. Easley, M. L. de Prado, and M. O’Hara, eds., 2013, High-Frequency Trading: New Realities for Traders, Markets and Regulators, London: Risk Books. he rest of this discussion draws heavily from this source. 52. Performance Statistics from NASDAQ, accessed in November 2014, http://www.nasdaqtrader.com/Trader.aspx?id=Latencystats. 53. A. Greenspan, 2000, “Electronic Finance,” Remarks by Chairman Alan Greenspan at the Financial Markets Conference sponsored by the Federal Reserve Bank of Atlanta, Sea Island, Georgia. 54. A bank’s available capital may be adjusted by the amount of assets that cannot readily be employed, such as goodwill, intangible assets, property, equipment, and cash needed for daily operations. 55. M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, pp. 2201–2238.

Notes

173

56. D. Duie, 2010, “Presidential Address: Asset Price Dynamics with SlowMoving Capital,” he Journal of Finance, Vol. 65, No. 4, pp. 1237–1267. 57. Default losses are generally determined by the principal value on the bond, less the default recovery rate. 58. Alternatively, if the basis becomes materially positive, arbitrageurs sell credit default swap protection and simultaneously short the corporate bond. 59. M. Mitchell and T. Pulvino, 2012, “Arbitrage Crashes and the Speed of Capital,” Journal of Financial Economics, Vol. 104, pp. 469–490. 60. D. Duie, 2010, “Presidential Address: Asset Price Dynamics with SlowMoving Capital,” he Journal of Finance, Vol. 65, No. 4, pp. 1237–1267. 61. Financial Stability Report, June 2014, Bank of England, Issue No. 35. 62. BlackRock Investment Institute, September, 2012, “Got Liquidity?”, see, http://www.blackrock.com/investing/literature/whitepaper/got -liquidity-us-version.pdf 63. M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, pp. 2201–2238. 64. J. F. Coughenour and M. Saad, 2004, “Common Market Makers and Commonality in Liquidity,” Journal of Financial Economics, Vol. 73, pp. 37–39. 65. J. Dick-Nielsen, P. Feldhütter, and D. Lando, 2012, “Corporate Bond Liquidity before and ater the Onset of the Subprime Crisis,” Journal of Financial Economics, Vol. 103, pp. 471–492.

4

Asset Pricing and Market Liquidity

1. F. A. Longstaf, 2004, “he Flight-to-Liquidity Premium in U.S. Treasury Bond Prices,” Journal of Business, Vol. 77, No. 3, pp. 511–526. 2. H. Chen, G. Noronha, and V. Singhal, 2004, “he Price Response to the S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation,” he Journal of Finance, Vol. 59, No. 4, pp. 1901–1930. 3. We utilize simpliications of some of the more general models from T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: heory, Evidence, and Policy, New York: Oxford University Press.

174

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4. Y. Amihud and H. Mendelson, 1986, “Asset Pricing and the Bid-Ask Spread,” Journal of Financial Economics, Vol. 17, pp. 223–249. 5. Y. Amihud and H. Mendelson, 1986, “Liquidity and Stock Returns,” Financial Analyst Journal, Vol. 42, pp. 43–48. 6. G. M. Constantinides, 1986, “Capital Market Equilibrium with Transaction Costs,” Journal of Political Economy, Vol. 94, Issue 4, pp. 842–862. 7. R. Roll, “A Simple Implicit Measure of Bid-Ask Spread in an Eicient Market,” 1984, he Journal of Finance, Vol. 39, No. 4, pp. 1127–1139. 8. H. R. Stoll, 2000, “Frictions,” he Journal of Finance, Vol. 55, No. 4, pp. 1479–1514. 9. A. S. Kyle, 1985, “Continuous Auctions and Insider Trading,” Econometrica, Vol. 53, No. 6, pp. 1315–1336. 10. Y. Amihud and H. Mendelson, 1986, “Asset Pricing and the Bid-Ask Spread,” Journal of Financial Economics, Vol. 17, pp. 223–249. 11. Y. Amihud and H. Mendelson, 1980, “Dealership Markets: Market Making with Inventory,” Journal of Financial Economics, Vol. 8, pp. 31–53. 12. A. S. Kyle, 1985, “Continuous Auctions and Insider Trading,” Econometrica, Vol. 53, No. 6, pp. 1315–1335. 13. D. Duie, N. Gârleanu, and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847. 14. Diamond P., 1982, “Aggregate Demand Management in Search Equilibrium,” Journal of Political Economy, Vol. 90, No. 5, pp. 881–894. 15. he assumption of repeated trade diferentiates the search-and-bargaining model from the labor market coconut model. 16. A consol bond is a bond with no maturity date, but it pays interest forever. Another example of a perpetual security would be a stock that pays dividends for an indeinite period. 17. he liquidity premium in this context can be thought of as an illiquidity discount. 18. D. Duie, 2010, “Presidential Address: Asset Pricing Dynamics with Slow-Moving Capital,” he Journal of Finance, Vol. 65, No. 4, pp. 1237–1267.

Notes

175

19. A. Shleifer and R. W. Vishny, 1997, “he Limits to Arbitrage,” he Journal of Finance, Vol. 52, No. 1, pp. 35–55. 20. We follow the model in which the arbitrageur is allowed to invest only part of his resources at date 0 and “save” the rest of his resources to intervene at date 1. he more complex model, however, does not ofer any additional insights and is not discussed further here. See T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: heory, Evidence, and Policy, New York: Oxford University Press. 21. his type of arbitrage is denoted as performance-based arbitrage, A. Shleifer and R. W. Vishny, 1997, “he Limits to Arbitrage,” he Journal of Finance, Vol. 52, No.1, pp. 35–55. 22. A. Shleifer and R. W. Vishny, 1992, “Liquidation Values and Debt Capacity: A Market Equilibrium Approach,” he Journal of Finance, Vol. 47, No. 4, pp. 1343–1366. 23. he aggregate supply from arbitrageurs sell orders at date 1 is ϕ 1 2 ∫0 ϕ (i)di = 2 ϕ . 24. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, pp. 375–410. 25. Under the CAPM, idiosyncratic market risk or company speciic risk is not rewarded with higher expected return. 26. T. Foucault, M. Pagano, and A. Roëll, 2013, Market Liquidity: heory, Evidence, and Policy, New York: Oxford University Press. 27. T. Chordia, R. Roll, and A. Subrahmanyam, 2000, “Commonality in Liquidity,” Journal of Financial Economics, Vol. 56, pp. 3–28. 28. L. Pastor and R. F. Stambaugh, 2003, “Liquidity Risk and Expected Stock Returns,” Journal of Political Economy, Vol. 111, No. 3, pp. 642–685. 29. Richard Bookstaber, former head of risk management at Salomon Bros., 2007, “Wall Street’s money machine breaks down: he subprime mortgage crisis keeps getting worse-and claiming more victims,” Fortune, November 26, p. 49. 30. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” he Society for Financial Studies, pp. 2202–2238. 31. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, pp. 375–410, and

176

32.

33.

34.

35.

36.

5

Notes

K. Lee, 2011, “he World Price of Liquidity Risk,” Journal of Financial Economics Vol. 99, pp. 136–161. B. Hagströmer, B. Hansson, and B. Nilsson, 2013, “he Components of the Illiquidity Premium: An Empirical Analysis of US Stocks 1927–2010,” Journal of Banking and Finance, Vol. 37, pp. 4476–4487. Note that Hagströmer, Hansson, and Nilsson implemented a conditional version of the LCAPM to allow for time variation in the risk premia. See B. Hagströmer, B. Hansson, and B. Nilsson, 2013, “he Components of the Illiquidity Premium: An Empirical Analysis of US Stocks 1927–2010,” Journal of Banking and Finance, Vol. 37, pp. 4476–4487. R. Goyenko, C. Holden, and C. Trzcinka, 2009, “Do Liquidity Measures Measure Liquidity?” Journal of Financial Economics, Vol. 92, No. 2, pp. 153–181. S. Kim, and K. Lee, 2014, “Pricing of Liquidity Risks: Evidence from Multiple Liquidity Measures,” Journal of Empirical Finance, Vol. 25, pp. 112–133. his section follows the derivation in T. Foucault, M. Pagano, and A. Roëll, 2013, Market Liquidity: heory, Evidence, and Policy, New York: Oxford University Press.

Stories of Liquidity and Credit

1. Lawrence Fisher spearheaded the development and maintenance of the Center for Research in Security Prices (CRSP) databases in the 1960s at the University of Chicago, Booth School of Business, which formed the foundation for decades of empirical research in inancial economics. 2. L. Fisher, 1959, “Determinants of Risk Premiums on Corporate Bonds,” Journal of Political Economy, Vol. 67, No. 3., pp. 217–237. 3. While other aspects of bonds such as call and put provisions, tax efects, sinking fund payments, and so forth are also interesting from a pricing and risk management perspective, much work has already been done in these areas, so we will focus on the credit and liquidity aspect of bonds. For a detailed discussion on other aspects F. Farbozzi

Notes

4.

5.

6.

7.

8. 9.

10.

11.

12.

177

and S. V. Mann, 2012, he Handbook of Fixed Income Securities, eighth edition, New York: McGraw-Hill. A. Shleifer and R. W. Vishny, 2011, “Fire Sales in Finance and Macroeconomics,” he Journal of Financial Perspectives, Volume 52, No. 1, pp. 29–48. Robert, C. Merton, 1974, “On the Pricing of Corporate Debt: he Risk Structure of Interest Rates,” he Journal of Finance, Vol. 29, Issue 2, pp. 449–470. J-Z. Huang and M. Huang, 2012, “How Much of the CorporateTreasury Yield Spread Is Due to Credit Risk?” Review of Asset Pricing Studies, Vol. 2, No. 2, pp. 153–202. More information on the Trade Reporting and Compliance Engine (TRACE) can be found on FINRA’s website. See http:// www.finra.org/industry/compliance/markettransparency/trace /corporatebonddata/. J. Bao, J. Pan, and J. Wang, 2011, “he Illiquidity of Corporate Bonds,” he Journal of Finance, Vol. 66, No. 3, pp. 911–946. A distinctive feature of most ixed-income markets it that trading takes place in the OTC market, which is dominated by a limited number of market makers. Finding a buyer for a given position can be time consuming and risky because it depends on the willingness of a market maker committed to providing liquidity. he search-andbargaining model discussed in chapter 4 is particularly relevant for gaining insight into liquidity risk involved in OTC trading. However, the search-and-bargaining model, while useful from a structural market perspective, is limited because it does not take into account corporate default and credit risks. A. Krishnamurthy and A. Vissing-Jorgensen, 2010, “he Aggregate Demand for Treasury Debt,” Journal of Political Economy, Vol. 120, No. 2, pp. 233–267. Amy K. Edwards, Lawrence E. Harris, and Michael S. Piwowar, 2007, “Corporate Bond Market Transaction Costs and Transparency,” he Journal of Finance, Vol. 62, pp. 1421–1451. J. Bao, J. Pan, and J., Wang, 2011, “he Illiquidity of Corporate Bonds,” he Journal of Finance, Vol. 66, No. 3, pp. 911–946.

178

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13. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” Working Paper. 14. A well-functioning interbank market is critical for any OTC trading and bond market trades in particular because of dealers’ reliance on the interbank market as a means of managing inventory, as discussed in chapter 3. 15. G. R. Dufee, 1999, “Estimating the Price of Default Risk,” Review of Financial Studies, Vol. 12, pp. 187–226. 16. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” Working Paper. 17. D. Bongaerts, F. de Jong, and J. Driessen, 2011, “Derivative Pricing with Liquidity Risk: heory and Evidence from the Credit Default Swap Market,” he Journal of Finance, Vol., 66, Issue, 1, pp. 203–240. 18. V. Niederhofer and M. F. M. Osborne, 1966, “Market Making and Reversal on the Stock Exchange,” Journal of the American Statistical Association, Vol. 61, pp. 897–916. 19. J. Bao, J. Pan, and J. Wang, 2011, “he Illiquidity of Corporate Bonds,” he Journal of Finance, Vol. 66, No. 3, pp. 911–946. 20. N. Friewald, R. Jankowitsch, and M. G. Subrahmanyam, 2012, “Illiquidity and Credit Deterioration: A Study of Liquidity in the US Corporate Bond Market during Financial Crises,” Journal of Financial Economics, Vol. 105, pp. 18–36. 21. J. Dick-Nielsen, P. Feldhutter, and D. Lando, 2011, “Corporate Bond Liquidity before and ater the Onset of the Subprime Crisis,” Journal of Economic Literature, Vol. 103, pp. 471–492. 22. N. Friewald, R. Jankowitsch, and M. Subrahmanyam, 2012, “Illiquidity or Credit Deterioration: A Study of Liquidity in the US Corporate Bond Market during Financial Crises,” Journal of Financial Economics, Vol. 105, pp. 18–36. 23. Other liquidity proxies include bond characteristics such as the amount issued, coupon, maturity, and age. In general, liquidity decreases with a bond’s age and maturity, but increases with its issuance size. hese are static measures and will typically drop out of a panel regression model of spread changes.

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24. he statistical signiicance of the zero-return measure is very low. It may not be meaningful measure. 25. Amy K. Edwards, Lawrence E. Harris, and Michael S. Piwowar, 2007, “Corporate Bond Market Transaction Costs and Transparency,” he Journal of Finance, Vol. 62, pp. 1421–1451. 26. A. Nashikkar, M. G. Subrahmanyam, and S. Mahanti, 2011, “Liquidity and Arbitrage in the Market for Credit Risk,” Journal of Financial and Quantitative Analysis, Vol. 46, Issue 3, pp. 627–656. 27. D. Duie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement, and Management, Princeton, NJ: Princeton University Press. 28. Robert C. Merton, 1974, “On the Pricing of Corporate Debt: he Risk Structure of Interest Rates,” he Journal of Finance Vol. 29, pp. 449–470. 29. L. Chen, D. A. Lesmond, and J. Wei, 2007, “Corporate Yield Spreads and Bond Liquidity,” he Journal of Finance Vol. 62, No.1, pp. 119–149. 30. F. Longstaf, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market,” he Journal of Finance, Vol. 9, No. 5, pp. 2213–2253. 31. Y. Amihud and H. Medelson, 1991, “Liquidity, Maturity, and the Yields on U.S. Treasury Securities,” he Journal of Finance, Vol. 46, pp. 1411–1425. 32. As an alternative, an investor could buy a string of short-term securities that mature in sequence. Such a policy will, however, expose the investor to reinvestment risk because his horizon is larger than the longer of the securities’ maturity. 33. J. Bao, J. Pan, and J. Wang, 2011, “he Illiquidity of Corporate Bonds,” he Journal of Finance, Vol. 66, No. 3, pp. 911–946. 34. D. Duie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement and Management, Princeton, NJ: Princeton University Press. 35. R. A. Jarrow and P. Protter, 2004, “Structural versus Reduced Form Models: A New Information Based Perspective,” Journal of Investment Management, Vol. 2, No. 2, pp. 1–10. 36. F. Longstaf, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default

180

37.

38.

39. 40. 41. 42.

43.

44. 45. 46.

47.

Notes

Swap Market,” he Journal of Finance, Vol. 9, No. 5, pp. 2213–2253. D. Duie and K. J. Singleton, 1999, “Modeling Term Structures of Defaultable Bonds,” he Review of Financial Studies, Vol. 12, Issue 4, pp. 687–720. he default intensity is related to the risk-neutral probability of default. It is assumed that default occurs at the irst arrival of a riskneutral Poisson process whose intensity process is λ. F. Longstaf, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market,” he Journal of Finance, Vol. 9, No. 5, pp. 2213–2253. R. Jarrow, 2001, “Default Parameter Estimation Using Market Prices,” Financial Analysts Journal, September/October. his assumption allows us to separate the expectations of a product into a product of expectations. D. Duie, 1999, “Credit Swap Valuation.” Financial Analysts Journal, January/February. he mathematical detail of this type of solution is beyond the scope of this book. he interested reader is referred to Appendix A in D. Duie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement and Management, Princeton, NJ: Princeton University Press. Note that the credit default swap premium may be a biased measure of the default component in corporate spreads. See D. Duie, and Jun Liu, 2001, “Floating-Fixed Credit Spreads,” Financial Analysts Journal, Vol. 57, pp. 76–87. P. Veronesi and L. Zingales, 2010, “Paulson’s Git,” Journal of Financial Economics, Vol. 97, pp. 339–368. Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” he Journal of Finance, Vol. 67, No. 2, pp. 391–428. R. Merton, 1974, “On the Pricing of Corporate Debt: he Risk Structure of Interest Rates,” he Journal of Finance, Vol. 29, No. 2, pp. 449–470. he irm’s assets are assumed to be a random process whose logarithm is normally distributed, which ensures that the assets in the model are nonnegative.

Notes

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48. F. Black and M. Scholes, 1973, “he Pricing of Options and Corporate Liabilities,” Journal of Political Economy, Vol. 81, No. 3, pp. 637–654. 49. F. Black and J. C. Cox, 1976, “Valuing Corporate Securities: Some Efects of Bond Indenture Provisions,” he Journal of Finance, Vol. 31, No. 2, pp. 351–367. 50. H. Leland and K. Tot, 1996, “Optimal Capital Structure, Endogenous Bankruptcy and the Term Structure of Credit Spreads,” he Journal of Finance, Vol. 51, No. 3, pp. 987–1018. 51. Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” he Journal of Finance, Vol. 67, No. 2, pp. 391–429. 52. he Poisson distribution is oten used in inance to model the occurrences of random events. he irst practical application of the distribution was in 1898, when Ladislaus Bortkiewicz was tasked with investigating the number of soldiers in the Prussian army who were accidentally killed by horse kicks. 53. He and Milbradt developed a version of the HX structural model in which market liquidity arises due to search and bargaining in the OTC market. See Z. He and K. Milbradt, 2012, “Endogenous Liquidity and Defaultable Bonds,” Working Paper, for a discussion on default bond pricing, and chapter 4 for a discussion on a search and bargaining model of market liquidity. 54. he interested reader is referred to the paper by Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” he Journal of Finance, Vol. 67, No. 2, pp. 391–429 for the details behind the derivation of these equations. 55. H. Chen, R. Cui, Z. He, and K. Milbradt, August 2014, “Quantifying Liquidity and Default Risks of Corporate Bonds over the Business Cycle,” Working Paper.

6

Financial Regulation and Liquidity Risk Management

1. P. L. Bernstein, 2005, Capital Ideas: he Improbable Origins of Modern Wall Street, Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada.

182

Notes

2. V. W. Fang, T. H. Foe, and S. Tice, 2009, “Stock Market Liquidity and Firm Value,” Journal of Financial Economics, Vol. 94, pp. 150–169. 3. A. Bervas, May 2006, “Market Liquidity and its Incorporation into Risk Management,” Banque de France, Financial Stability Review, No. 8. 4. Calculations by L. Laeven and F. Valencia using data from the banking crisis database. See speech by S. Ingver, Chairman, Basel Committee on Banking Supervision and Governor, Sveriges Riksbank at the Federal Reserve Bank of Chicago, November 2014. 5. Speech by S. Ingver, Chairman, Basel Committee on Banking Supervision and Governor, Sveriges Riksbank at the Federal Reserve Bank of Chicago, November 2014. 6. SIFMA, US Research Quarterly, 2014, “Equity and Other Markets,” US Research Quarterly, Second Quarter. 7. Y. Amihud and H. Mendelson, 1991, “Liquidity, Asset Prices and Financial Policy,” Financial Analyst Journal, Vol. 47, No. 6, pp. 55–66. 8. K. Balakrishnan, M. B. Billings, B. Kelly, and A. Ljungqvist, 2014, “Shaping Liquidity: On the Causal Efects of Voluntary Disclosures,” he Journal of Finance, Vol. 69, No. 5, pp. 2237–2278. 9. E. Barreto, 2014, “Alibaba IPO Ranks as World’s Biggest ater Additional Shares Sold,” Reuters, September 22. 10. For a discussion on the management of funding liquidity risk, see John. C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada. 11. A. Bangia, F. Diebold, T. Schuermann, and J. Stroughair, 1998, “Modeling Liquidity Risk with Implications for Traditional Market Risk Measurement and Management,” Working Paper, he Wharton Financial Institutions Center. 12. Liquidity-adjusted VaR was proposed by A. Bangia, F. Diebold, T. Schuermann, and J. Stroughair, 1999, “Liquidity on the Outside,” Risk, No. 68. Our treatment follows that of John Hull. See John C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada.

Notes

183

13. A. Bervas, 2006, “Market Liquidity and Its Incorporation into Risk Management,” Banque de France, Financial Stability Review, No. 8. 14. Basel Committee on Banking Supervision, 2009, Guidelines for Computing Capital for Incremental Risk in the Trading Book, Trading Book Group of the Basel Committee on Banking Supervision, Bank of International Settlements, July. 15. D. Brigo and C. Nordio, 2010, “Liquidity-Adjusted Market Risk Measure with Stochastic Holding Period,” Working Paper. 16. N. Gârleanu and L. H. Pedersen, 2007, “Search-and-Matching Financial Markets: Liquidity and Risk Management,” AEA Papers and Proceedings, Vol. 97, No. 2, pp. 193–197. 17. A. Shleifer and R. Vishny, 2011, “Fire Sales in Finance and Macroeconomics,” Journal of Economic Perspectives, Vol. 25, No. 1, pp. 29–48. 18. F. Duarte and T. M. Eisenbach, 2014, “Fire-Sale Spillovers and Systemic Risk,” Federal Reserve Bank of New York Staf Reports, no. 645, October 2013; rev. May 2014. 19. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” he Society for Financial Studies, pp. 2202–2238. 20. Fire sales are not limited to crisis periods. For example, poorly performing mutual funds without signiicant cash reserves may also be forced to sell holdings quickly. Harvard University professors Joshua Coval and Eric Staford looked at the efect on the drop in stock prices when mutual funds with holdings of a particular stock are forced to sell due to investor withdrawals. See J. Coval and E. Staford, 2007, “Asset Fire Sales (and Purchases) in Equity Markets,” Journal of Financial Economics, Vol. 86, pp. 479–512. 21. Z. He, I. G. Khang, and A. Krishnamurthy, 2010, “Balance Sheet Adjustments during the 2008 Crisis,” International Monetary Fund Economic Review, Vol. 58, pp. 118–156. 22. H. Till, 2006, EDHEC Comments on the Amaranth Case: Early Lessons from the Debacle, EDHEC Risk and Asset Management Research Centre. 23. One of the factors in the Liquidity Adjusted Capital Asset Pricing discussed in chapter 4 quantiies this particular component of market liquidity.

184

Notes

24. Corporate liquidity risk management usually involves matching cash lows using a gap analysis, which is a related but diferent aspect. 25. D. Bisias, M. Flood, A. Lo, and S. Valavanis, 2012, “A Survey of Systemic Risk Analytics,” US Department of Treasury, Oice of Financial Research, Working Paper. 26. Final Rule and Interpretive Guidance to Section 113 of the DoddFrank Consumer Protection Act. 27. he Group of Ten members was established in 1974 and it included 12 countries: Belgium, Canada, France, Germany, Italy, Japan, Luxemburg, the Netherlands, Sweden, Switzerland, United Kingdom, and the United States. 28. Bank of International Settlements, 2014, “A Brief History of the Basel Committee,” Basel Committee on Banking Supervision, BIS. 29. Empirical research showed that funding liquidity and market liquidity are diferent but related concepts. Reduced funding liquidity can cause market liquidity and vice versa. See M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, No. 6, pp. 2201–2238. 30. he discussion and examples provided in the sections titled “he liquidity coverage ratio” and “he net stable funding ratio” are from similar discussions by John Hull, see John C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada. 31. International Monetary Fund, April 2011, “How to Address the Systemic Part of Liquidity Risk,” Global Financial Stability Report, Chapter 2. 32. Wall Street Journal, 2014, “U.S. Regulators Tweak Final Liquidity Rule for Large Banks,” September 3. 33. Wall Street Journal, 2014, “U.S. Regulators Tweak Final Liquidity Rule for Large Banks,” September 3. 34. Blackrock Investment Institute, September 2012, “Got Liquidity?” Blackrock, http://www.blackrock.com/investing/literature/whitepaper /got-liquidity-us-version.pdf

Notes

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35. W. Bagehot, [1873] 1999, Lombard Street: A Description of the Money Market, London: King report; New York: Wiley. 36. J. C. Stein, 2013, “Liquidity Regulation and Central Banking,” Remarks by Jeremy C. Stein, Member, Board of Governors of the Federal Reserve System, 2013 Credit Markets Symposium. 37. Western Union Internal Memo, 1876, Quotes on Value.

Index AAA bonds, 21, 23, 118, 122, 126 ABS. See asset-backed securities (ABS) ABX, 31 Acharya, Viral (professor), 101, 103 Acharya-Pederson pricing model, 101–3 Acropolis (Athens), 3 adverse markets, 92 adverse selection, 9, 45–6, 48, 53 in dealer markets, 85 limit orders and, 57, 60–1 liquidity and, 32 ater-the-fact intervention, 161 agency models, 93–101, 108 agency notes, 26 aggregate market risks, 12, 75 aggregate supply vs. aggregate demand, 5–6 agora (ancient Greek market), 3 Akerlof, George (Nobel laureate), 44 Alibaba (company), 147 alternative trading platforms, 46 Regulation ATS, 51, 62–3 Amaranth (hedge fund), 154 American Finance Association, 68, 83 American Sands Energy Corporation (AMSE), 61 American Society of Finance, 84 Amihud, Yakov (professor), 17, 78, 84, 126 Amihud measure, 84, 107–8, 124 Amihud-Mendelson model, 78 antiquity, 2–3, 6 Apple (company), 6, 44, 46 arbitrage. See no arbitrage arbitrage opportunities, 11–12, 91–2, 94 arbitrage portfolio, 95 arbitrageurs, 92–100, 173n58, 175n20 access to capital of, 11 CDS-bond basis and, 68–70 in frictionless economy, 33 principal/agent problem and, 34 Argentina, 117 Arrow, Kenneth (economist), 6 Arrow-Debreu model, 6–7 artiicial intelligence, 41 ask prices. See bid and ask prices ask quotes, 47, 50, 53 asset pricing models. See under speciic models asset-backed commercial paper (ABCP), 5, 24, 27–8

Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), 24 asset-backed securities (ABS), 20, 44, 48, 118 ABCP and, 27–8 during crisis, 144 margins on, 153 assets. See under speciic assets asymmetric information, 31–2, 44–6, 61, 76 Bagehot on, 9 commonality in liquidity and, 105 in dealer markets, 85 in equity markets, 136 inancial disclosures and, 146 Gorton on, 20 on OTC markets, 86 asymmetric information costs, 61 asymmetric information model, 169n14 Athens, 3 auctions, 50–1, 57–8 Treasury, 66, 91 Walrasian, 7–8, 39, 41–2, 60–1 See Walrasian auction autocorrelations, 83 automated trading, 40–1, 61, 63–6 available stable funding factor (ASF), 158 Bagehot, Walter (essayist), 9, 161 bail outs, 22, 136 balance sheets, 12, 70–1, 117–18 vs. ABCP, 27 Basel III and, 158–61 liquidity of, 32 repurchase agreements and, 29, 33–4 risk weight of, 145 bank accounts, risk-free, 87 bank asset risk, 7 Bank of America, 58 Bank of England, 2 bank portfolios, 32 bank-based systems, 4–5 bankers, 3, 21 bankruptcy, 27, 134, 138. See also Lehman Brothers bankruptcy (2008); Long-Term Capital Management (LTCM) bankruptcy (1998) banks commercial, 20, 67, 152–4 as inancial intermediaries, 6–7 interbank markets, 20, 30–2, 120, 178n14

188

Index

banks—Continued investment, 20, 22, 29, 67, 146–7 See also central banks; speciic banks Bao, Jack (professor), 122–3, 127 Barclays LX (ATS), 63 bargaining power, 55–6, 59, 119 in search-and-bargaining model, 86, 89–91, 110, 113 barter, 2–4 Basel Accord, 67 Basel Committee on Banking Supervison, 151, 155–6 Basel III (regulation), 70, 145, 155–60 basis, 34, 76, 173n58 CDS-bond, 68–70, 118–20 See also yield spreads basis points, 69, 76, 115, 122, 139–40 Bayesian learning process, 43 BBB bonds, 126 Bear Stearns, 72, 160 behavioral economics, 77, 93 Bernake, Ben (chairman of Federal Reserve), 1, 35 Bernstein, Peter (author), 143 Bervas, Arnaud, 144 bespoke securities, 48–9 Biais, Bruno, 58 bid and ask prices, 53–4, 80–1 in equilibrium price, 42–3 market makers and, 89–90 on NASDAQ, 51 on OTC markets, 85 in search-and-bargaining model, 88–9, 109–11 bid quotes, 47, 50, 53 bid-ask spreads, 19, 37, 42–4, 54–8, 108 asset pricing and, 78–83 bargaining power and, 113 basis trading and, 120 Black on, 9–10 as compensation, 85 frequency of transactions and, 60 high-margin securities and, 71–2 information symmetry and, 45–6 LCAPM and, 101 liquidity and, 119 in portfolio risk, 147 public listing and, 146 in Roll measure, 122 in search-and-bargaining model, 77, 86, 88–91 Stoll on, 61–2 transaction costs and, 135 VaR and, 148–51 bilateral trade, 40, 54–6 Black, Fisher (economist), 9, 16, 134 Black-Scholes option pricing model, 9, 12, 33, 134 Bloomberg (real-time feed), 46–7, 54 bond indices, 117

bond markets, 2, 33, 48, 130, 178n14 corporate, 53–4, 70, 120, 125, 141 liquidity in, 115–27, 133–6, 139, 141 bond prices, 12 bond rating, 125 bond risk premium, 36 bond yield spread, 139 bond-credit default swap basis. See credit default swap (CDS)-corporate bond basis bonds consol, 87, 109, 174n16 defaultable, 116, 125, 127–32, 141 government, 2, 76, 115, 157 investment-grade, 34, 69–70, 116, 125–6, 139–41 long-term, 125–6 municipal, 48, 56 securitized, 21 zero-coupon, 93–4, 127, 130, 133 See also corporate bonds; Treasury bonds Bortkiewicz, Ladislaus, 181n52 boundary conditions, 138 Bretton Woods system, 4, 156 Brigo, Damiano (researcher), 151 brokerage irm analysts, 116 brokerage frees, 78 broker-dealers, 52, 54, 63, 152–3, 161 brokers, 47, 54, 59, 62, 67–8 broker-speculators, 67 Brownian motion, 129–30, 136 Brunnermeier, Markus (professor), 71–2, 152–3 buy and sell orders, 43–4, 51, 57, 59–60, 65 arbitrage and, 97–100 dark pools and, 62 in Roll measure, 81–3 in search-and-bargaining model, 110 buy side, 54, 56, 85 buy-and-hold investors, 28 buying market, 2 call auctions, 58 capital, 4, 33–7, 66–71 access to, 7, 11 arbitrage and, 91–7, 99–101, 108 of banks, 172n54 bonds and, 134 credit risk and, 117 in frictionless market, 75 interventions to, 161 market structures and, 41 in NSFR, 158 in repurchase agreements, 29–30 risk and, 154, 156 withdrawal of, 45 capital asset pricing model (CAPM), 16, 101–3 capital costs, 70, 143, 145–6

Index Capital Ideas (Bernstein), 143 capital investments, 10, 29 capital markets, 1, 6–7, 37, 158 capital structure of irms, 128, 135 capitalization, 83 Carney, Mark (governor of Bank of England), 2 cash, 24–7, 31, 66–7, 153 as daily liquid asset, 25 as high-quality liquid asset, 157, 159–60 liquid bankers and, 21 See also money cash equities, 49, 65 cash lows, 33, 127, 136–7, 161, 184n24 in absolute pricing, 12 in asset pricing model, 131 in classical pricing theory, 13 during crisis, 144 in Diamond coconut model, 87 in discounted cash low model, 79–80, 117–18 expected, 5, 15 liquid assets and, 35–6 in search-and-bargaining model, 111–12 in Shleifer-Vishny model, 94–5 short-term, 37 in traditional asset pricing, 75 of Treasury bonds, 91 cash holdings, 29 cash outlays, 145 cash securities, 33 cash-in-the-market pricing, 37 Center for Research in Security Prices (CRSP) databases, 176n1 central banks, 1, 40, 155–6, 158 Basel III and, 160–1 interbank markets and, 30, 32 central clearing, 40, 49, 73 central order books, 51 centralized markets, 3, 50, 55, 116 illiquidity costs in, 59 limit order markets and, 57 cheapest-to-deliver option, 120 Chen, Long (professor), 125–6 Chicago, Illinois, 64 Chicago Board of Trade, 40 Chicago Board Options Exchange Volatility Index (CBOE VIX), 127 China, 147 Chi-X (multilateral trading facility), 46 Citadel Investment Group, 154 Citi Match (ATS), 63 Citigroup, 147 classical inance theory, 39, 161–2 classical pricing theory, 13 Clearing House Interbank Payments System (CHIPS), 40

189

CLS Bank, 40 CME Globex platform, 59 CME Group, 50–1, 59, 64 coins, 2–3 collateral, 20, 26–7, 55, 92 in interbank markets, 31 land as, 3–4 for lender of last resort, 161 margins on, 157 in repo agreement, 153 repurchase agreements and, 29–30 for Treasury bonds, 76 collateralized borrowing, 66–8, 71–2 collateralized debt markets, 22 collateralized debt obligations (CDOs), 5, 21, 48, 118 markets for, 49–50, 116 collateralized mortgage obligations, 50 commercial banks, 20, 67, 152–4 commercial papers, 67, 157 asset-backed, 5, 24, 27–8 commission, 143 Committee on Banking Regulations and Supervisory Practices, 151, 155–6 commodities, 5–6, 49, 64 common risk factors, 17 commonality of exposures, 155 in liquidity, 101, 103–8, 126–7 comparable investments, 12 competition, 45–6, 51–4, 59, 142 in inancial sector, 5 in Walrasian auction, 8 complete markets, 17, 44 computers, 40–1, 58 conidence level, 147, 149 consol bonds, 87, 109, 174n16 Constantinides, George (professor), 78 consumption, 6–7, 13–16, 36, 154, 169n10 consumption-based models, 16 continuation value of irms, 26 continuous trading, 58 control systems, 144 convertible bond arbitrage, 33 corporate bond markets, 53–4, 70, 120, 125, 141 corporate bond-credit default swap basis, 68–70, 119–20 corporate bonds, 33–4, 47, 66, 115–30 Basel III and, 161 CDS-bond basis, 68–70, 119–20 default risk in, 76 in LCR, 157 liquidity of, 72 on OTC markets, 50, 116 risk premium on, 36, 115 TRACE and, 170n26 yield spreads for, 119, 122–6

190

Index

corporate debt, 116, 118 corporate inance, 12 corporate policies, 144 corporate securities, 36 corporate valuation, 133, 143–4 correlation risk, 36 costs. See under speciic types of cost counterparties, 70, 147, 157 inding of, 55–6, 62 in OTC markets, 77, 85–6 counterparty default, 20, 26 counterparty risk, 31–2, 34, 69, 120, 156 coupon bonds, 131 coupon payments, 76, 136–8 Coval, Joshua (professor), 183n20 Cox, John (economist), 134 Cox-Ingersoll-Ross (CIR) model, 129 creative destruction, 66 credit, 55, 99, 133, 161 bonds markets and, 118, 122 as margin, 66–7 in reduced-form model, 129 in regression model, 124 credit card receivables, 31 credit default swap (CDS)-corporate bond basis, 68–70, 119–20 credit default swaps, 33–4, 116, 119–22, 133, 180n43 as proxy for default risk, 125 credit exposure, 33–4, 49–50 credit lines, 4–5, 28, 31, 67 credit ratings, 21, 67, 86, 139 credit risks, 23, 68, 115–16, 156 in bonds market, 117–20, 122, 125, 127, 142 of defaultable bonds, 128, 132 in HX model, 134–5, 139 liquidity risks and, 151, 161 in reduced-form model, 133 in regression model, 123 credit spreads, 116, 140–1 Credit Suisse Crossinder (ATS), 63, 147 credit-gap indicators, 155 creditworthiness, 36, 56, 115 crisis periods, 92–3, 99–100, 106, 161 ire sales and, 152–5, 183n20 in liquidity, 19, 56, 152–3, 154 See also global inancial crisis (2008) currency market, 54 current markets, 10 customer limit order markets, 58 daily liquid assets, 25–6 daily transaction prices, 84 dark pools/dark ATS, 62–3 De Soto, Hernando (economist), 4 dead capital, 4 dealer banks, 40, 67, 70

dealer markets, 48, 50–6, 85 dealers, 1, 8, 52–6, 62–3 bargaining power of, 89 in bid-ask spreads, 82, 135 broker-dealers, 152–3, 161 capital requirements of, 67–8, 70 at NASDAQ, 42 in search-and-bargaining model, 85–6, 110 See also inancial intermediaries; market makers Debreu, Gerard (economist), 6 debt, 67, 116–18, 133–7, 141–2 ballooning, 70–1 capital and, 143, 153–4 credit risk and, 128, 133 equity and, 156–7 in HX model, 139 long-term, 26 market, 50 short-term, 27–8, 36 See also collateralized debt obligations (CDOs) debt market, 20, 105, 117 default, 23, 27, 36, 158 in asset pricing model, 131–3 on corporate bonds, 68, 137 counterparty, 20, 26 credit risk and, 127 liquidity and, 117, 161 master agreements and, 55 in option-pricing model, 134 recovery payments and, 138 in reduced-form model, 128–9 See also Lehman Brothers default boundary, 134, 141 default intensity, 129, 132, 180n37 default losses, 173n57 default premium, 117, 138, 141 default risks, 23, 28, 32, 36 in asset pricing, 76 for corporate bonds, 141–2 risk premium and, 115 yield spread and, 125–6 defaultable bonds, 116, 125, 127–32, 141 delayed feeds, 47 delevering, 153 demand, 98–9, 109, 153 for cash, 31 for low-risk assets, 154 for repo agreements, 29 See also supply and demand demand deposits, 6, 25, 67 Demsetz, Harold (economist), 7, 42, 59, 61 Depository Trust Company, 40 deposits, 1, 4–7, 29–31, 157–60 derivative market, 117–18, 120 derivatives, 33–4, 48–50, 58 OTC, 40, 55

Index Deutsche Börse (German stock market), 46, 50–1, 147 Diamond, Peter (Nobel laureate), 86–8 Diamond coconut model, 86–8, 174n15 direct obligations, 26 discount cash low model, 79–80, 117–18 discount factors, 12–17, 75 discount rate, 131, 137–8 discounted future value, 80, 111–12 discounted present value, 127 dislocations, 19–20, 31, 33, 144 dissemination, 47–8, 52, 72–3 dividends, 78–9 Dodd-Frank Act (2010), 155 Dow Jones Industrial Average, 169n12 drachmas, 2 Duie, Darrell (professor), 55–6, 68, 77, 85–6 Easley, David (professor), 43 Economic Club of New York, 35 Economic Sciences, 4, 7 economic theory, inancial, 32 Economist (magazine), 1–2 efective spreads, 81 eicient market hypothesis, 10 eicient markets, 10–11, 41, 44–5, 65, 94 electronic access networks, 40, 53 electronic communication networks (ECNs), 51–3 electronic limit order books, 47, 50–1, 58–9 electronic markets, 52, 63–4, 66 electronic trading systems, 40, 57–8, 64 e-minis, 2, 64 end-of-day prices, 121–2 endowments, 92–3 equilibrium, 6–13, 36, 45, 56, 60 in Amihud-Mendelson model, 78 arbitrage and, 98–100 between supply and demand, 75 equilibrium prices. See fundamental value equity, 17, 64–5, 76, 139, 145–6 in asset pricing, 6, 133–4 capital and, 143, 153 cash, 49 credit risk and, 128 debt and, 156 ire sales and, 154 of homes, 5 in HX model, 134–5 issuance of, 36 limit order markets and, 58 on OTC markets, 50 in S&P 500 index, 92 trades in, 40–1 equity capital, 67, 92, 153 equity exchanges, 52–3 equity markets, 52, 65, 117–18, 136 Eurodollars, 50

191

Euronext (stock market), 46, 50–1, 59 Europe, 46, 117 European Central Bank, 31 European Markets in Financial Instruments Directive (“MiFID” 2007), 64 event-based time, 64 exchange rates, 4, 156 exchanges, 40, 47, 50–3, 59, 83 costs of, 145 dark pools and, 62 in National Market System, 64 vs. OTC markets, 85 exchange-traded environment, 53, 85, 119 exchange-traded funds (ETFs), 2, 5 execution costs, 48, 55–6, 81, 85 execution prices, 50, 56–8, 80 in dark pools, 63 in liquid markets, 8–9 in Walrasian market, 60–1 exogenous costs, 61, 75, 78, 101, 107–8 exogenous risk, 36 exogenous shocks, 94, 134 exotic derivatives, 48 expected future rollover efects, 134–5 expected returns, 83, 92–3, 136–7, 145 in CAPM, 16–17, 102, 105 liquidity and, 34–5, 79 exposure, credit, 33–4, 49–50 Facebook, 59 failure of no-arbitrage principle, 77. See also no arbitrage fair value, 11 Fama, Eugene (Nobel Prize winner), 11 Fang, Vivian (professor), 143–4 fear gauge, 127 federal government agencies, 26 federal insurance, 23 Federal Reserve, 1, 11, 35, 66–7, 161 ABCP market decline and, 28 AMLF, 24 Federal Reserve Banks, 24, 40, 152 Fedwire, 40 inance theory, 32, 39, 41, 77, 161–2 inancial collapse in Southeast Asia, 92 inancial crisis. See global inancial crisis (2008) inancial disclosures, 146 inancial economics, 9 Financial Industry Regulatory Authority (FINRA), 48 inancial intermediaries, 6, 21–2, 48, 53, 70 as auctioneers, 39 bid-ask spreads and, 45–6, 50 capital requirements and, 41 ire sales and, 153 inventory risks of, 105 on OTC markets, 85

192

Index

inancial intermediaries—Continued risk aversion and, 154 shocks and, 37 See also dealers; market makers inancial markets, 1, 4–11, 30, 91–2 disruptions in, 155–6 in global crisis, 19 rollover risk and, 141–2 Financial Times (newspaper), 32 inancing costs, 55, 87 ire sales, 21, 26–7, 105, 144–5 crisis and, 152–5, 183n20 First Investment Trust (index fund), 5 irst-passage models, 134 Fisher, Lawrence (professor), 36, 115, 176n1 ixed cost, 6 ixed-income futures, 66 ixed-income markets, 2, 177n9 ixed-income portfolios, 147–8 ixed-income securities, 70–1, 118 ixed-income trading, 65–6, 116 lash crisis (May 6, 2010), 1–2, 169n12 light-to-quality phenomenon, 141 loating currencies, 4 loor-based exchange trading, 40, 51, 57–8, 64 foreign exchange, 53, 64, 86, 116–17 for-proit entities, 51 Foucault, hierry (professor), 58, 104 fragmentation of markets, 7, 62–3, 65–6 France, 46 Francis Emory Fitch, Inc., 121 Frankfurt Bourse, 59 free market economics, 4 freezes, 22, 31–2 frictionless markets, 5–6, 33, 75, 134–5 CAPM and, 101 CDS-bond basis in, 68 risk management and, 147–8 Shleifer-Vishny model and, 94 Walrasian auction as, 8 frictions, 8, 33, 89 in equity markets, 136 in OTC markets, 119 real-world, 6, 35 Friedman, Milton (economist), 4 Friewald, Nils (professor), 123–4 fund managers, 56, 93–5 hedge fund, 11, 13, 35, 121 mutual fund, 47, 94 fund portfolios, 24 fund sponsors, 23 fundamental returns, 82 fundamental securities, 33 fundamental threshold, 134 fundamental value (general equilibrium price), 7–8, 14–16, 39, 88–90, 130

arbitrage and, 91, 93–4, 96–8, 100 asset pricing and, 19, 75, 79–80 in HX model, 134–5, 139 liquidity premium and, 127–8 in modiied Roll measure, 121 in no arbitrage, 10–11, 33–4 risk calculation and, 148 in Roll measure, 82–3 of security, 42, 44 funding, 6, 22, 27, 99–100 funding liquidity, 20, 28–9, 160, 184n29 market liquidity and, 37, 72, 152, 154 future contracts, 11 future value, 6 futures, 11, 17, 40–1, 48, 64–6 Eurodollar, 50 Gârleanu, Nicolae (professor), 34, 85–6 Gaussian normal distributions, 149 general equilibrium economy, 6–8. See also equilibrium general equilibrium price. See fundamental value Germany, 46 Getco (ATS), 63 global inancial crisis (2008), 19–36, 140, 144–5, 160–2 bonds markets and, 117–18, 122, 127 capital during, 68–70 illiquidity in, 1–2, 156 origins of, 152–3 global markets, 4, 59, 144 globalization, 41, 58 Glosten, Lawrence (professor), 45, 58, 61 gold standard, 3–4 Goldman Sachs, 54, 63, 147 Gorton, Gary (Yale professor), 20, 29–31, 167n20 government bonds, 2, 76, 115, 157 government data releases, 66 government guarantees, 144 government-sponsored enterprises, 26 Great Depression, 106 Greenspan, Alan (Chairman of Federal Reserve), 66 gross domestic product (GDP), 144 gross market returns, 105 gross yield, 23 Grossman, Sanford (economist), 9 Group of Ten (G10), 155–6, 184n27 Hagströmer, Björn (professor), 106, 176n33 haircuts. See margins Hansson, Björn (professor), 106, 176n33 He, Zhiguo (professor), 134–5, 139, 181n53 hedge fund managers, 11, 13, 35, 121 hedge funds, 20, 67–9, 92, 94 arbitrage strategies of, 33, 92

Index collapse of, 153–4 See also Long-Term Capital Management (LTCM) bankruptcy (1998) hedgers, 12 hedging, 55, 85, 105, 119, 147 hedging needs, 48 heteroskedasticity, 129 Hicks, John Richard (economist), 4, 9 high rollers, 87–9, 109, 111 high-frequency trading, 40–1, 61, 63–6 high-margin securities, 71 high-quality liquid assets, 157 high-speed alternative trading systems (ATSs), 63 holding costs, 42–3, 87–9, 113, 119 holding periods, 77–8, 89, 106, 148, 151 of bonds, 126 Holmstrom, Bengt (MIT professor), 27 homes, 5, 40, 85, 143 horizon efect, 121 hot potato trading, 1–2 households, 54 HX model, 135–9, 181n53 idiosyncratic risks, 14–15 IGT Posit (ATS), 63 illiquid markets, 8, 117, 130, 144, 156 illiquidity costs, 59 illiquidity discount (liquidity afect), 37, 96, 100 immediacy, 7–10, 42, 61–2, 68 imperfect searches, 85 index funds, 5, 92 index pop, 76 ineiciencies of markets, 65 information, 34–5, 43–7, 53–7, 63–6, 169n10 acquiring of, 6 market structures and, 50 price formation and, 39 public vs. private, 41 symmetric, 146 value of security and, 82 in Walrasian auction, 8 See also asymmetric information information costs, 3, 48, 61 initial public oferings (IPO), 147 insider trading, 53 insolvency, 29, 156 solvency, 30, 133, 160–1 Instinet (ATS), 63 institutional arrangements, 41, 44, 49, 50, 55 institutional cash pools, 27 institutional fund managers, 56 institutional investors, 40, 52, 56, 92, 146 in dark pools, 62 high-frequency trading and, 66

193

insurance, 14, 15, 23, 25, 68 deposit, 7 title, 40 interbank markets, 20, 30–2, 120, 157, 178n14 interdealer markets, 54, 86–7, 110 interest rates, 26, 85–7, 90, 120, 147–8 default risk and, 129 interbank markets and, 32 liquidity premium and, 117 uncertainty of, 127 intermediaries. See inancial intermediaries International Monetary Fund (IMF), 144 International Swap and Derivative Association (ISDA), 55 intertemporality, 8 inventories, 55–6, 70, 105, 161 in Diamond coconut model, 87 in high-frequency trading, 64 in price formation, 42–3 in search-and-bargaining model, 85–6, 110 investment accounts, 46 investment banks, 20, 22, 29, 67, 146–7 Investment Company Act, 23–4 investment horizon, 126 investment managers, 85, 93 investment-grade bonds, 34, 69–70, 116, 125–6, 139–41 investors. See institutional investors ITS order routing system, 64 J.P. Morgan, 69, 72, 147, 154 Jankowitsch, Rainer (professor), 123–4 Jarrow, Robert (professor), 130 Journal of Finance, 146 Journal of Financial Markets, 58 Keynes, John Maynard (economist), 4, 9, 19 Knight (ATS), 63 Kyle, Albert (professor), 43, 84, 108 land, 3–4 latency issues, 65 latent liquidity risks, 19–20, 27, 32 law of one price, 10, 91 learning problem, 43 least-square optimization procedure, 132 Lehman Brothers bankruptcy (2008), 23, 30–2, 107, 157, 160 bonds market and, 115 Reserve Primary Fund and, 20 Leland, Hayne (professor), 134 lender of last resort, 161 Lesmond, David (professor), 125–6 level premium, 104, 106–7 leverage, 29, 35, 91–2, 139–40, 153 Basel III and, 156

194

Index

liabilities, 26, 67, 71, 128, 154 Basel III and, 160 Libor-OIS spread, 31 limit orders, 42, 47, 50–1, 57–61, 171n31 in dark pools, 63 Roll measure and, 81 limit price, 58 linear models, 123 liquid asset basket, 25–6 liquid bankers, 21 liquid markets, 8–10, 35, 37, 120, 124 bid-ask spreads in, 45, 80 mean-variance framework and, 101 NYSE as, 52 price impact and, 84 reliance on, 143 liquidation, 92, 95–7, 100–1, 105 crisis and, 151 of LTCM, 115 portfolio value and, 147–8 of SIFIs, 155 transaction costs and, 149 liquidation costs, 126, 141 liquidation price, 148 liquidation value, 138 liquidity backstop, 28 liquidity costs, 106, 119, 126, 148, 150 liquidity coverage ratio (LCR), 155–7 liquidity crises, 19, 56, 152–3, 154 liquidity efect (illiquidity discount), 37, 96, 100 liquidity level premium, 104, 106–7 liquidity premium, 17, 96–100, 116–20, 174n17 of bonds, 141, 161 during crisis, 144 fundamental value and, 127–8 in HX model, 135, 138–9 LCAPM and, 104, 106 in reduced-form model, 133 in search-and-bargaining model, 86, 88–9 liquidity proiles, 17, 48–9 liquidity proxies, 178n23 liquidity risk premium, 15, 103–7, 154, 176n33 on corporate bonds, 115, 117, 119 liquidity risks, 19–23, 26–8, 144–5, 150–2, 155–7 of asset, 12, 77 of bonds, 126–7, 131, 136, 141 in CAPM, 103–7 CDS and, 120 credit risk and, 151, 161 exogenous vs. endogenous, 148 funding and, 37 latent, 19–20, 27, 32 in mean-variance framework, 101 in reduced-form model, 129 systemic, 101, 160 See also risk management

liquidity shocks, 20–1, 134–7, 139–41 liquidity suppliers, 47, 59, 62, 65 liquidity-adjusted capital asset pricing model (LCAPM), 77, 101–4, 106–8, 176n33 liquidity-adjusted value-as-risk (VaR), 148–51 liquidity-provision policies, 141 Liquinet (ATS), 63 listed derivatives, 49 lit markets, 63 live capital, 4 loans, 3–5, 27–9, 67, 159–61 as assets, 17 illiquidity and, 35 in interbank markets, 31 timing risks and, 91–2 loan-to-value ratios, 5 London Bourse, 59 London International Financial Futures and Options Exchange (LIFFE), 50–1 London Stock Exchange (LSE), 50–1 Longstaf, Francis (professor), 35, 76, 125, 129–30 long-term assets, 28, 125–6, 156 long-term borrowing, 67 Long-Term Capital Management (LTCM) bankruptcy (1998), 17, 92, 105, 107, 115 as liquidity spiral, 153–4 long-term debt, 26 long-term funding, 158 long-term investors, 50 low rollers, 87–8, 109, 111 Lydians, 2–3 macroeconomy, 12, 117–18, 125, 155 managers. See under speciic managers manual markets, 62 margin calls, 37, 92 margins (“haircuts”), 29–30, 33–4, 66–72, 139 arbitrage and, 91 on corporate bonds, 70 crisis and, 153, 157 repo, 167n20 market capitalization, 83 market depth, 9, 45, 84, 119, 147 market eiciency, 41, 93 market impact measures, 84 market liquidity risks. See liquidity risks market makers, 7–9, 78, 84, 110–11 access to capital of, 72 as auctioneers, 39, 42 competition between, 45 in dealer markets, 54 in Easley and O’Hara setup, 43 electronic, 40, 63 human, 65 information from, 47 on NASDAQ, 51

Index net capital rule and, 67–8 on OTC markets, 50, 70, 85–6, 177n9 in search-and-bargaining model, 87–91 underwriters as, 147 See also dealers; inancial intermediaries market meltdowns, 17 market microstructure, 9, 45, 84, 108, 119, 155 Market Microstructure (Biais, Glosten, and Spatt), 58 market orders, 57–61, 63, 171n31. See also buy and sell orders; limit orders market portfolios. See portfolios market power, 47, 59, 88 market returns, 16, 78, 101, 104–6. See also returns market structures, 5–7, 50–3, 64–5, 83 adverse selection and, 46 of dealer markets, 56 liquidity and, 44–5, 60, 143 of OTC markets, 57, 116 technological advances and, 40–1, 50 market value, 55, 61, 95, 133 of bonds, 115, 135 marketability of bonds, 115 of inancial instruments, 143 of security, 36 market-based prices, 25 market-clearing prices, 42, 58, 60, 98–9, 102 market-to-book value ratios, 144 master agreements, 40, 55 matching engine protocols, 65 maturity mismatch, 158 maturity security, 126 maturity structure, 26–7 McCabe, Patrick (economist), 23 mean-variance framework, 77, 101 medium-term funding, 158 Mendelson, Haim (professor), 78 Merchant of Venice (Shakespeare), 3 Merton, Robert (Nobel laureate), 7, 116, 125 Merton model, 116, 133 Metrick, Andrew (Yale professor), 29–30, 167n20 midprices, 81, 83, 147–50 midquotes, 79, 81–3 mid-term notes, 27 Milbradt, Konstantin, 181n53 Milgrom, Paul (professor), 45 Millennium Bridge of London, 36–7 Miller, Merton (Nobel laureate), 9 minting of coins, 3 mispricing, 11, 20, 33, 57–8 arbitrage and, 91–100 Mitchell, Mark (professor), 33 Mithal, Sanjay, 129–30 modernization, 143

195

modiied Roll measure, 121, 127 monetary policies, 1, 4, 30, 117–18 money, 1–5, 32, 67, 115, 151 arbitrage and, 91–4 as liquid asset, 17, 39 substitutes for, 4–5 time value of, 11, 13, 127 See also cash money managers, 34, 94 money market funds (MMFs), 5, 19–20, 22–6, 40, 183n20 Monto Carlo simulation, 149 Morgan Stanley MS Pool (ATS), 63 mortgage-backed securities (MBS), 5, 27, 32, 144, 153 mortgages, 27–8, 40, 157, 160 illiquidity and, 35 origins of, 164n15 as stores of value, 5 multicolinearity, 123 multilateral trading facility (MTF), 46 multiplier efect, 151 municipal bonds, 48, 56 mutual fund managers, 47, 94 mutual funds, 5, 19–20, 22–6, 40, 183n20 National Association of Securities Dealers Automated Quotations (NASDAQ), 42, 51–5, 65, 145 dealers on, 67–8 Facebook on, 59 vs. NYSE, 61–2, 83 National Association of Security Dealers (NASD), 116 National Market System (NMS). See Regulation National Market System negative basis, 34 Neis, Eric, 129–30 Nelson, Bill (economist), 161 neoclassical inancial ediice, 2 net asset value (NAV), 22–4 net capital rule, 67 Net Stable Funding Ratio (NSFR), 156, 158–9 New York City, New York, 35, 64 New York Stock Exchange (NYSE), 47–8, 50–3, 67, 106, 145 electronic vs. loor trading at, 58–9 vs. NASDAQ, 61–2, 83 New York Times (magazine), 121 Niederhofer, Victor, 121 Nilsson, Birger (professor), 106, 176n33 Nixon, Richard (US president), 4 no arbitrage, 10–11, 32–5, 91–5, 130 asset pricing and, 77 CDS-bond basis and, 120 See also arbitrageurs

196

Index

Noe, homas (professor), 143–4 noise traders, 43, 84 no-load index mutual funds, 5 nonasset-backed commerical papers, 28 nonexecution, 57 nonlisted securities, 51 nonmarketability of assets, 35–6 nonzero basis, 34 Nordio, Claudio (researcher), 151 Northern Rock, 157 NYSE Arca all-electronic exchange, 58–9 October 1987 crash, 107 of-balance entities, 28 of-balance sheet items, 158 of-the-run bonds, 91 O’Hara, Maureen (professor), 43 oil crises (1973, 1979), 107 one-period arithmetic returns, 16 on-the-run bonds, 91 opacity, 44, 47, 54 open access marketplaces, 59 open-ended investment companies, 24 operational charges, 67 opportunity costs, 55, 63, 77 option price, 9, 12, 33, 133–4 option valuation, 12, 135 order arrival process, 83 order books, 9, 121. See also limit orders order lows, 42–4, 46–7, 50–1, 64–5, 83–4 asymmetric information and, 169n14 in limit order markets, 61 price and, 54, 121 order processing costs, 53, 61 Ornstein-Uhlenbeck stochastic process, 129–31 overnight inancing, 29 over-the-counter (OTC) derivatives, 40, 55 over-the-counter (OTC) markets, 28, 35, 47–50, 85–6 adverse selection in, 60 Basel III and, 161 bid-ask spreads in, 89 vs. centralized markets, 116 vs. exchange-traded environments, 53, 85, 119 ixed-income markets and, 177n9 in HX model, 181n53 interbank markets and, 178n14 structure of, 57 trading in, 47–8, 70, 77 Pan, Jun (professor), 122–3, 127 paper companies, 27 payofs, 10, 12–15, 75, 80 in CAPM, 103 of convertible bonds, 33 limit orders and, 60

Pedersen, Lasse (professor), 34, 71–2, 85–6, 151 on asset pricing, 101, 103, 105 on margins and crisis, 152–3 pension funds, 92, 94 perfectly liquid markets, 8, 44, 75, 79, 101 Peru, 4 pledgability of assets, 27 Poisson occurrence, 135, 180n37, 181n52 policies, 53, 66, 161 corporate, 144–6 during inancial crisis, 19 liquidity-provision, 141 on MMFs, 23 monetary, 1, 4, 30, 117–18 pricing, 9 regulatory, 40, 71–3, 117–18, 142 See also regulations portfolio managers, 85, 119, 147 portfolio risks, 20, 23–4, 147, 150, 154 portfolio securities, 24–5 portfolios, 32–3, 95, 127, 141 in CAPM, 16 CDS-bond basis in, 69 of irms, 136 of LTCM, 92, 105 risk premium and, 103 VaR of, 147–51 posttrade transparency, 47–8, 170n26 premium. See liquidity premium; liquidity risk premium pretrade transparency, 47 price changes, 2, 9, 19, 120–1 decrease, 42, 76 order low and, 44 transaction volume and, 84 price concessions, 55, 61 price discounts, 92, 97, 116 price discovery, 7–8, 54, 60–3, 66 in asset pricing models, 17 transparency and, 47 price dispersion measure, 124 price formation, 7–8, 39, 41–4, 73, 169n14 in dark pools, 63 high-frequency trading and, 66 investors and, 60 on OTC markets, 85 price impact, 9, 37, 84, 100, 122 price priority, 57 price-quantity quotes, 43 prices. See under speciic prices price-setting agents, 42 pricing transparency, 2 primary markets, 46, 49 primary security dealers, 1 prime brokerage agreements, 40 principate (Rome), 3

Index priority rules, 58 product-investment decisions, 41 proitability, 45 proit-maximizing quote, 43 promissory notes, 67 property price, 155 proprietary trading desks, 92 Prussia, 181n52 public funds, 32 public listing, 146 Pulvino, Todd (professor), 33 quote depth, 148 quote rule, 42 quote-driven markets, 50, 53–4 quotes, 46–7, 50–4, 62–3, 65 railroad crash (1929), 115 random walk, 10, 66, 81–3, 120–1 ratings, credit, 21, 67, 86, 139 real estate, 39–40, 85, 143 subprime market, 20–1, 28–9, 31, 36, 152 realized value, 147–8 real-time feeds, 46–7 real-time information, 54 real-world arbitrage, 12, 92–3 real-world frictions, 6, 35 real-world markets, 8, 13, 39, 101–2 real-world transactions, 121, 124 receivables, 5, 27, 31 recovery rate, 138, 173n57 redemptions, 24–5, 33–5, 126, 139 reduced-form credit model, 128–9, 133 reinancing, 5, 20, 26, 117 of commercial papers, 157 of debt, 134, 141 of short-term debt, 36 regression model, 123–4, 127 Regulation Alternative Trading System (ATS), 51, 62–3 Regulation National Market System (NMS), 52–3, 64 regulations, 7, 39, 50–1, 67–8, 160–1 Basel III, 70, 145, 155–60 See also policies regulators, 19–20, 116, 119, 141–2 Basel III and, 70 defaulted Lehman debt and, 23 in Europe, 46 information from, 128, 155 VaR and, 147 vulnerabilities and, 155 regulatory policies, 40, 71–3, 117–18, 142 reinvestment risk, 126, 179n32 relative asset pricing, 12–13 repurchase (repo) agreements, 22, 25–7, 91, 153

capital and, 66–9 credit exposure and, 33–4 during crisis, 157 short-term, 29–30 repurchase (repo) clearing banks, 40 repurchase (repo) funding, 71–2 repurchase (repo) haircuts, 167n20 repurchase (repo) markets, 29–30, 70, 153 Reserve Primart Fund, 20, 23–4 residential mortgage obligations, 50 residential real estate, 39–40, 85 resiliency, 9, 24, 45, 60, 70 Resolution Trust Corporation, 76 retail deposits, 158, 160 retail investors, 25, 46–7, 56, 92, 146 return risk, 23, 148, 150 returns, 7, 12, 61–2, 76–9, 136–7 arbitrage and, 92–3 in CAPM, 102–4 illiquidity and, 32, 34–5 liquidity and, 36, 143, 152 market, 15–17, 78, 101, 104–8 in Roll measure, 81–3 VaR and, 149–50 See also expected returns Reuters (real-time feed), 46–7, 54 reverse purchase transaction, 91 risk aversion, 28, 42, 56, 154 risk limits, 151 risk management, 3–5, 26, 119, 133, 144 cash lows and, 184n24 liquidity and, 81, 151 VaR and, 147–8 yield spread and, 117 See also liquidity risks risk managers, 14, 133, 141, 147–8 risk premium. See liquidity risk premium risk weight of balance sheets, 145 risk-free inancing, 11 risk-free interest rates, 14–16, 79, 115, 127 annual, 11 asset value and, 136 in CAPM, 102 CDS-bond basis and, 119 in HX model, 138 in Shleifer-Vishny model, 94 riskless securities, 7, 79, 87 risks. See under speciic types of risk risk-sharing agents, 6–7 Roll, Richard (economist), 81–3 Roll measure, 81–3, 122, 124, 150 modiied, 121–2 rollover efects, 134–5, 141 rollover risk, 139, 141–2 Rome, 3 Rule 2a-7 (SEC), 22–4, 26

197

198

Index

rules. See policies; regulations Russian debt crisis (1998), 17, 92, 105, 151 savings, 6 Schumpeter, Joseph (economist), 66 search costs, 55–6, 86, 88–9 search eiciency, 90 search-and-bargaining model, 77, 85–8, 101, 108–13 vs. Diamond coconut model, 174n15 OTC market and, 177n9 search-based economy, 86–7 secondary markets, 24, 26, 49–50, 116, 141 for corporate bonds, 34, 115, 134–5 second-round spillover losses, 152 secured credit lines, 67 securities. See under speciic securities Securities and Exchange Act (1934), 146 Securities and Exchange Commission (SEC), 22–6, 42, 51–2, 169n12 ITS order routing system and, 64 net capital rule, 67 securities haircuts, 67 Securities Industry and Financial Markets Association (SIFMA), 21, 118, 145 securitization, 5, 20–2, 27 securitized assets, 21, 31, 35, 48 sell orders. See buy and sell orders sell side, 54, 56 senior management, 147 settlements, 15, 40 Shakespeare, William, 3 share prices, 135–6 shareholder base, 25 Sharpe, William (Nobel laureate), 16 Shleifer, Andrei (economist), 12, 93 Shleifer-Vishny model, 93–4, 108 shocks, 9, 22, 97, 104, 152 to bond market, 127, 135 capital and, 34, 37, 100–1 crisis and, 31, 144 exogenous, 94, 134 liquidity, 20–1, 134–7, 139–41 of public information, 65–6 resiliency and, 45 to supply and demand, 68, 70 short selling, 66, 102, 120 short-term agency notes, 26 short-term borrowing, 20 short-term cash inlows, 37 short-term credit markets, 27, 156 short-term debt, 27–8, 36 short-term inancing, 26, 30 short-term funding, 22, 27, 161 short-term loans, 28 short-term repurchase (repo) agreements, 29

short-term securities, 24, 125–6 short-term uncollateralized loans, 67 Shylock (character in Merchant of Venice), 3 slow-moving capital, 68 small business loans, 160 smart order routing systems, 46 solvency, 30, 133, 160–1 insolvency, 29, 156 Southeast Asia, 92 sovereign debt crisis, 117 Spatt, Chester, 58 specialists, 67–8, 72 speculative-grade bonds, 125–6, 139–41 speed of arbitrage, 35 of dissemination, 44, 47, 64 of execution, 56, 119 of return to equilibrium, 45 of trading, 58, 65 of transacting, 53, 55, 72–3 spillover losses, 152 spot markets, 91 spreads. See bid-ask spreads; yield spreads square-root model, 129–31 stable funding, 158–9 Staford, Eric (professor), 183n20 Standard and Poor’s (S&P) 500 equity index, 11, 76, 92, 127 standardization, 3, 48–9 Stigum’s Money Market (reference guide), 23 stochastic liquidity model, 102, 108 stochastic time horizon, 151 stochastic trading costs, 101 stock exchanges. See under speciic stock exchanges stock market crash (1929), 4 stock markets, 9–10, 17, 76, 143 stock prices, 12, 62, 121 stocks, 46, 72, 76, 78, 92 in Amihud measure, 84 as assets, 17, 40 vs. ixed-income securities, 116 liquid vs. illiquid, 17, 144 on NYSE vs. NASDAQ, 83 risky, 62 Stoll, Hans (professor), 61–2, 83 stores of value, 4–5 structural credit model, 128, 133 structured credit products, 20, 28 structured inance, 21–2, 47 structured securities, 35, 50, 153 student loans, 31 subordinate liabilities, 67 subprime market, 20–1, 28–9, 31, 36, 152 subprime residential mortgage-backed securities, 5

Index Subrahmanyam, Marti (professor), 123–4 supply and demand, 21–2, 42, 110, 130 aggregate, 5–6, 60–1 in general equilibrium economy, 7–8, 75, 97 shocks to, 68, 70 surplus, 110 syndication market, 35 systemic crisis, 31, 144, 161 systemic risks, 14–17, 78, 155, 160 compensation for, 101–2 of corporate bonds, 126–7 short-term funding and, 22 systemically important inancial institutions (SIFIs), 155 taxes, 25, 76, 87, 134 teaser rates, 5 technological advances, 40–1, 50–3, 63–6, 72–3 ten-year government agency bonds, 115 term repurchase agreements, 25, 29 term structure of bonds, 125, 133 “he Only Game in Town” (Bagehot), 9 theoretical arbitrage, 10, 34, 91–2 theory of general equilibrium, 6 third-party investors, 93, 95 Tice, Sheri (professor), 143–4 tick size, 48, 60–1, 65, 143–4 Tier 1 capital, 158, 160 Tier 2 capital, 158, 160 tightness, 9 time decay, 137, 147 time deposits, 25 time priority, 57 time value of money, 11, 13, 127 time-to-maturity, 137–8 timing risks, 91 Tirole, Jean (Nobel laureate), 27 title insurance, 40 Tot, Klaus (professor), 134 “Towards a Fully Automated Exchange” (Black), 9–10 trade deicit, 4 trade prices, 39, 47, 85, 89 of bonds, 130, 132–3 Trade Reporting and Compliance Engine (TRACE), 48, 116, 170n26 trade volume, 49, 54, 61, 66 in Amihud measure, 84 in Demsetz model, 7 liquidity and, 8, 169n12 on NYSE, 53 in regression model, 124 trade-by-trade prices, 121–2 traders, informed vs. uninformed, 39, 43–5, 47–8, 84, 169n10 trade-through rule, 52, 64, 169n15

199

trading costs, 8–9, 36, 49, 75 in asset pricing, 78 in CAPM, 101, 103 in HX model, 139 in Roll measure, 83 in search-and-bargaining model, 89 trading loor, 40 trading protocols, 50 trading queue, 60 trading ratio, 71 transacting, speed of, 53, 55, 72–3 transaction costs, 7, 59–60, 75–80, 126, 135–7 bond value and, 139 in CAPM, 102–3 in dealer markets, 85 liquidity as, 75, 107–8 in Merton model, 133 optimism on, 144 in Roll’s measure, 83 VaR and, 149 transaction low, 45 transaction prices, 8, 75, 81–5, 121–2 vs. fundamental values, 11 information on, 50 transaction reports, 48 transaction taxes, 78 transaction volume, 84 transparency, 2, 17, 46–9, 145–6 Treasury auctions, 66 Treasury bills, 79, 80 Treasury bonds, 66, 91, 115, 160 yields of, 76, 118, 125 Treasury debt, 118 Treasury securities, 25–6 tri-party repurchase (repo) and clearing agreements, 40 true beneit vs. true cost, 13 true value of security, 39, 43–4 UBS PIN (ATS), 63 uncertainty ABCP and, 28 during crisis, 20 of default, 129, 134 discount factors and, 14 of future interest rates, 127 of future prices, 17 in liquid markets, 8–9 of liquidity, 81, 101–2 in liquidity shortages, 4 of market value, 133 MMFs and, 22 vs. money, 39 in order low, 42 risk management of, 5 value of assets during, 44

200

Index

uncertainty—Continued Yahoo Finance and, 46 underwriters, 146–7 unsecured rates, 32 US households mortgages of, 5 US Treasury, 70–1 used cars, 44 utility function, 13 value in antiquity, 2–3 continuation, of irms, 26 discounted future, 80, 111–12 discounted present, 127 equity, 134–6 stores of, 4–5 time, of money, 11, 13, 127 true, of security, 39, 43–4 See also fundamental value; market value value-at-risk (VaR), 147–51 Vanguard Group, 5 variance swaps, 48 Vietnam War, 4 Vishny, Robert (economist), 12, 93 volatility, 15, 62, 64, 149, 151 of assets, 58, 136, 139, 147–8 volume of trade. See trade volume

volume-weighted average prices, 63 voluntary disclosure, 146 walk, random, 10, 66, 81–3, 120–1 Wall Street, 4 Walras, Leon (economist), 7–8 Walrasian auction, 7–8, 39, 41–2, 60–1 Wang, Jiang (professor), 122–3, 127 weekly liquid assets, 25–6 Wei, Jason (professor), 125–6 white noise, 82 wholesale debt markets, 20 wholesale deposits, 158, 160 wholesale funding. See repurchase (repo) agreements World War II, 106 Xiong, Wei (professor), 134–5, 139 Yahoo Finance, 46 yield, 23, 28, 70, 120, 146 of bonds, 68–9, 76, 91, 132–3 search for, 117 yield spreads, 36, 115, 117–19, 139–41 of corporate bonds, 34, 68, 119, 122–6 yield-to-maturity, 138 zero-coupon bonds, 93–4, 127, 130, 133 zero-return measure, 124, 179n24