The Financialization of Commodity Markets: Investing During Times of Transition 1137465573, 9781137465573

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The Financialization of Commodity Markets

The Financialization of Commodity Markets Investing during Times of Transition Adam Zaremba

THE FINANCIALIZATION OF COMMODITY MARKETS

Copyright © Adam Zaremba, 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: 978–1–137–46557–3 Library of Congress Cataloging-in-Publication Data Zaremba, Adam, 1985– The financialization of commodity markets : investing during times of transition / Adam Zaremba. pages cm Includes bibliographical references and index. ISBN 978–1–137–46557–3 (hardback : alk. paper) 1. Commodity exchanges. 2. Commodity futures. I. Title. HG6046.Z37 2014 332.6328—dc23

2014039897

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

To my brilliant and beautiful wife Patrycja, who deserves all the credit

Contents List of Illustrations

ix

Introduction

xv

1 Asset Allocation in Commodity Markets

1

2 Passive Investment Strategies in Commodity Markets

9

3 Active Investment Strategies in Commodity Markets

47

4 Financialization of Commodity Markets

101

5 Performance Measurement of Commodity Investments

159

6 Commodity Investments in Financialized Markets—a Study

189

Conclusions

209

Notes

211

Bibliography

215

Index

247

Illustrations Figures 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14

Production of grain and trading with grain futures in the United States Cotton production and trading with futures contracts in the New York Cotton Exchange from 1871 to 1897, 2002 Global volume of trading with futures and options in the years 1998–2013 (million units) Average annualized returns to spot commodities and collateralized commodity futures from 1959 to 2004–2012 Commodity excess returns and roll returns, December 1982–May 2004 Rolling futures contracts in the “elder” generation of indices of the commodity markets Rolling futures contracts in the “younger” generation of indices of the commodity markets Rolling futures contracts for indices that provide exposure to the entire forward curve Risk premium according to the normal backwardation theory Performance of the commodity market indices during the period from 1998–2014 (rebased indices August 1998 = 100) Average annual rate of returns and standard deviations in the commodity market 1998–2014 AUM of the managed futures industry in the years 1980–2013 (billion USD) Division of futures contracts investment strategies Moving average—an example Sample buy and sell signals based on moving average Comparison of moving averages Intersection of moving averages as a transactional signal Breakout strategies for sample quotations Head and shoulders pattern on the sample graph Shape of Japanese candlesticks Bear market truncation on the sample graph RSI oscillator for sample quotations Relative value strategies The diversification of portfolios of futures and stocks Classification of managed futures indices

11 12 14 16 19 23 24 25 31 42 42 49 57 59 59 60 62 63 64 65 66 67 68 72 80

x

Illustrations

3.15 Managed futures indices from December 1999 to October 2010 4.1 Notional outstanding amounts of OTC forwards and swap instruments in the world in the years 1998–2014 (USD million) 4.2 Notional outstanding amounts of OTC forwards, swap instruments, and options in the world in the years 1998–2014 (USD million) 4.3 Market value of OTC derivatives in the world in the years 1998–2014 (USD million) 4.4 Number of open positions in and trading volume of commodity futures contracts in the years 1993–2014 (million) 4.5 Share of instruments based on commodities in the global derivatives market (%) 4.6 Ratio of the volume of futures contracts to the number of open positions on the cereals markets in the years 2000–2011 4.7 Percentage of futures transactions in the cereal market made via electronic platforms (2004–2011) 4.8 Non-commercial investors in the CBOT wheat market 4.9 Non-commercial investors in the WTI crude oil market 4.10 Non-commercial investors in the COMEX copper market 4.11 Non-commercial investors in the COMEX gold market 4.12 Non-commercial investors in the NYMEX natural gas market 4.13 Market Composition, Open Interest, and the Price of Oil 4.14 Structure of the investors in the commodity markets in the years 1990–2012 4.15 Percentage of open positions held by the index investors in the years 2004–2011 4.16 Cumulative index flow and S&P GSCI Agriculture and Livestock Excess Return Index, January 2006– October 2009 4.17 Copper prices against net positions of the noncommercial investors 4.18 Changes in the prices of selected commodities in the years 2000–2011 (percent) 4.19 Traded vs. non-traded (RIND Index) commodity prices (index, rebased end-2007=100) 4.20 Linear correlation coefficient between WTI oil and soy in the years 1988–2011 (annual periods, daily rates of return) 4.21 Linear correlation coefficient between WTI oil and cattle in the years 1988–2011 (annual periods, daily rates of return) 4.22 Linear correlation coefficient between WTI oil and copper in the years 1988–2011 (annual periods, daily rates of return)

86

104

104 105

105 106

107 108 109 109 110 110 111 111 112 113

117 117 120 120 123 123 124

Illustrations

4.23 Linear correlation coefficient between WTI oil and cotton in the years 1988–2011 (annual periods, daily rates of return) 4.24 Average correlations of indexed and off-index commodities, 1973–2011 4.25 Volatility of stock prices and correlation coefficient in the commodity markets in the years 2000–2011 4.26 Inflation and its variance in the United States in the years 1973–2011 4.27 Sources of profit in the commodity futures market in the years 1970–2010 (percentage points) 4.28 Cumulative returns on strategies based on term structure in financialized and non-financialized markets 4.29 Cumulative returns on strategies based on momentum in financialized and non-financialized markets. 4.30 Correlation coefficient between commodities and equities in the period from December 1994 to June 2011 4.31 Correlation coefficient between commodities and bonds in the period from December 1994 to June 2011 4.32 Average monthly rate of return on CISDMCAW* rolled over five-year periods in the years 1984–2011 4.33 Average monthly rate of return on CISDMCEW* rolled over five-year periods in the years 1984–2011 4.34 Average monthly rate of return on BARCCTA* rolled over five-year periods in the years 1984–2011 4.35 Average monthly rate of return on MLMCITR rolled over five-year periods in the years 1984–2011 4.36 Average monthly rate of return on MLMCI rolled over five-year periods in the years 1984–2011 4.37 Cumulative arithmetic rates of return on individual components of profit on the SPGSCI index in the period from January 1970 to June 2011 4.38 Cumulative arithmetic rates of return on individual components of profit of the JPMCCI indices in the period from December 1991 to June 2011 4.39 Cumulative arithmetic rates of return on individual components of profit of the DJUBS indices in the period from December 1991 to June 2011 4.40 Cumulative arithmetic rates of return on individual components of profit of the SPGSCI indices in the period from December 1991 to June 2011 4.41 Cumulative arithmetic rates of return on individual components of profit of the UBSCMCI indices in the period from December 1997 to June 2011 4.42 Cumulative arithmetic rates of return on individual components of profit of the MLCX indices in the period from December 1991 to June 2011

xi

124 125 127 128 129 130 131 144 145 148 148 149 149 150

152

153

154

155

156

157

xii

Illustrations

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11

Graphical representation of the Sharpe ratio Security market line Jensen’s alpha Treynor ratio Measure by Modigliani and Modigliani Graphical representation of GH1 Graphical representation of GH2 Conditional value at risk Drawdown statistics Graphical representation of RFR Expansion of the efficient frontier due to the introduction of a new asset class to the universe of the available investments 6.1 Efficient frontier in the stock, bond, and commodity markets (expected return vs. standard deviation) 6.2 Efficient frontier in the stock, bond, and commodity markets (expected return vs. MVaR) 6.3 Efficient frontier in the stock, bond, and commodity markets (expected return vs. standard deviation) with parameter a = 0.37 p.p. 6.4 Efficient frontier in the stock, bond, and commodity markets (expected return vs. MVaR) with parameter a = 0.37 p.p. 6.5 Efficient frontier in the stock, bond, and commodity markets (expected return vs. standard deviation) with parameter a = 0.185 percent 6.6 Efficient frontier in the stock, bond, and commodity markets (expected return vs. MVaR) with parameter a = 0.185 p.p. 6.7 Efficient frontier in the stock, bond, and commodity markets (expected return vs. standard deviation) for the rising correlation scenario 6.8 Efficient frontier in the stock, bond, and commodity markets (expected return vs. MVaR) for the rising correlation scenario 6.9 Efficient frontier in the stock, bond, and managed futures markets in the expected return and standard deviation framework 6.10 Efficient frontier in the stock, bond, and managed futures markets in the expected return and MVaR framework

160 165 166 167 169 170 170 174 175 179

181 191 192

196 197

199 199

202

202

205 205

Tables 2.1

2.2

Global volume of trading with options and futures contracts in the world in 2013 broken down into the underlying instruments Global volume of trading with options and futures contracts in the world in 2013 by regions

13 13

Illustrations

The world’s largest markets of futures and options in terms of trading volume in 2013 2.4 Historical excess returns, December 1982–May 2004 2.5 Simulation of diversification returns for different numbers of instruments in the portfolio and for different volatility and correlation 2.6 Weights in the selected commodity indices in year 2014 2.7 Commodity market indices 3.1 AUM of managed futures by a subclass between the fourth quarter of 2014 and the second quarter of 2014 3.2 Definitions of the CTA, CPO, FCM, and IB 3.3 Comparison of the tasks and responsibilities of CTA and CPO 3.4 Levered and delivered returns by hedge fund strategy, 1997–2001 3.5 Characteristics of the managed futures indices 3.6 Correlations between managed futures indices from December 1999 to October 2010 3.7 Rate of return on managed futures indices from December 1999 to October 2010 3.8 Rebalancing bias—computational example 3.9 Biases on the databases and CTA indices and the proposed measures for reducing or eliminating them 4.1 Performance of technical analysis in the light of previous studies 4.2 Indices used in the correlation analysis 4.3 Correlation coefficients between the stock market and the commodity market in the period from 1991 to 2011 4.4 Correlation coefficients between the bond market and the commodity market in the period from 1991 to 2011 4.5 Indices used in the study of the effectiveness of technical analysis 4.6 Rates of return on active and passive indices of futures funds in the years 1980–2011 4.7 Indices used in the study of changes in the sources of returns in the commodity markets 4.8 Sources of return on the SPGSCI index in the years 1970–2011 4.9 Sources of return on the JPMCCI indices in the period from December 1991 to June 2011 4.10 Sources of return on the DJUBS in the period from December 1991 to June 2011 4.11 Sources of return on the SPGSCI indices in the period from December 1991 to June 2011 4.12 Sources of return on the UBSCMCI indices in the period from December 1997 to June 2011

xiii

2.3

14 17

22 38 40 50 51 52 74 85 87 88 95 97 139 143 145 146 147 150 151 152 153 154 155 156

xiv

Illustrations

4.13 Sources of return on the MLCX indices in the period from December 1991 to June 2011 5.1 IR for negative rates of return—a calculation example 6.1 The indices used in the portfolio optimization with commodities and futures contract funds 6.2 Stocks, bonds and commodities—basic statistics 6.3 Mean-variance spanning test with passive investment strategies in the commodity markets—regression approach 6.4 Mean-variance spanning test with passive investment strategies in the commodity markets—simulations 6.5 Stocks, bonds, and commodities—descriptive statistics for a = 0.37 p.p. 6.6 Mean-variance spanning test for the commodity market with parameter a = 0.37 p.p. 6.7 Mean-variance spanning test with passive investment strategies in the commodity markets with parameter a = 0.37—simulations 6.8 Stocks, bonds, and commodities—descriptive statistics for a = 0.185 percent 6.9 Mean-variance spanning test for the commodity market with parameter a = 0.185 p.p. 6.10 Mean-variance spanning test with passive investment strategies in the commodity markets with parameter a = 0.37 p.p.—simulations 6.11 Mean-variance spanning test for commodities for the growing correlation scenario 6.12 Mean-variance spanning test with passive investment strategies in the commodity markets for the scenario of modified interdependence between the asset classes— simulations 6.13 Stocks, bonds and managed futures—basic statistics 6.14 Regression-based tests of mean-variance spanning for managed futures 6.15 Mean-variance spanning test with active investment strategies in the commodity markets—simulations

157 163 190 191 192 193 195 197

197 198 200

200 203

203 204 206 206

Introduction Since the publication of the paper titled “Facts and Fantasies about Commodity Futures” by Gorton and Rouwenhorst (2004), no other class of assets has gained as much popularity as commodities. Formerly classified as niche investment products, they have made their way into the portfolios of pensioners, students, and common investors. The level of activity of investors in commodity markets has been increasing at an impressive rate. According to the data of the Bank of International Settlements (BIS), the number of open items in commodity-related futures contracts has increased from slightly above 20 million in 1993 to almost 160 million 17 years later. The turnover did not fall behind. In 1993, the participants of the market completed transactions for about 40 million futures contracts, whereas in 2010, the amount was almost 600 million, which means that the volume had increased by as much as fifteen times.1 The interest of investors ran parallel with the development of information and analytical products, as well as with the increase in the number of publications in the media and scientific press. Speculators in commodity markets have never been received well by politicians and citizens. While the majority of the academic world appreciate the benefits of the presence of speculators in commodity markets, such as the fact that they ensure liquidity, the very notion of the “speculator” holds negative connotations for the rest of society. The Collins Dictionary of English Synonyms mentions four synonyms for the word “speculation”: gamble, risk, gambling, and hazard (Szado 2011). However, the increase in speculative activity in the commodity markets had not provoked any fears until 2008, when the oil prices exceeded 100 USD per barrel. At that point, speculation transformed into “excessive speculation” in accordance with the Collins definition (2011), which states that “excessive speculation occurs when the prices of commodities become painful for voters.” While the organs of public administration have begun to inspect the functioning of commodity markets and the activities of speculators closely, there has been a revival in ideas for limiting the activity of speculators in commodity markets. High prices of commodities—including oil in particular—have resulted in the increased interest of the scientific world in the role of financial investors in commodity markets and their significance to the fixing of prices. The importance of the impact that investors have on the activity of commodity markets has been noticed. A great deal of emphasis was placed on the increase in the prices of food and energy, which threaten the functioning of

xvi

Introduction

entire societies in poorer parts of the world. However, relatively little attention was focused on the importance of the high increase in the speculative activity of the investors themselves. This book is an attempt to describe and explore in a complex manner how structural changes caused by the increased activity of financial investors on commodity exchanges have influenced investment conditions in the relevant markets. A particular emphasis was placed on the strategic allocation of assets within the investment portfolio and the benefits resulting therefrom. Therefore, the book is intended to bridge a significant gap in the literature on the subject. The analysis has been performed from the point of view of a global investor. Investments in commodity markets are relatively complicated. In contrast with the classic equity or bond markets, there is no simple method for getting exposition to a commodity market, and particular “investment vehicles” used for that purpose may differ significantly between one another. In addition, there is no single source of profit, but several independent and uncorrelated ones. What is more, there is no unambiguous data, and all available data is charged with numerous burdens. Finally, the investment performance measures laid out in the literature are not fully adjusted to the character of a commodity market. Apart from the analysis of the functioning of such measures, the assumed purpose required collecting complete information concerning the effects of the increased significance of financial investors. In the subject literature, the process of the increase in the significance of financial investors is usually referred to as financialization. All of the above-mentioned problems constitute the subject of this work. The effects of the increase in the significance of investors in commodity markets may potentially be multidimensional and, for example, related to the formation of price bubbles, change of market term structure, increase of correlation, or decline in the efficiency of selected investment strategies. Their presence, to a reasonable extent, may even entirely question the relevance of the practice of undertaking investments in commodity markets. The discussed issues are therefore considerably important especially for investors, both individual and institutional. The essential purpose of this work is to answer the question of whether, under the conditions of structural changes in commodity markets resulting from the increased significance of financial investors, investments in commodities remain relevant both as individual deposits and as a part of a larger investment portfolio, contributing to the increase of efficiency—that is, to the improvement of the profit-to-risk relation. In other words, does investing in commodities still make sense in light of the fundamental changes to the markets? The book is divided into six chapters. Chapter 1 contains the definitions of basic notions related to the strategic allocation of assets in commodity markets. It also describes the definitions and classifications of assets used in the literature. Furthermore, the chapter contains the differentiation between the notions of benchmark and strategic or tactical asset allocation. It also presents an overview of investments in

Introduction

xvii

commodity markets, together with an indication of the ways in which one may gain exposition to them. Chapter 2 focuses on the characteristics and functioning of passive deposits in commodity markets, understood as investments in indices of commodity futures contracts. The history of development of the futures market is followed by an analysis of different sources of profits related to commodity futures contracts. It also presents key theories and hypotheses explaining the functioning of long-term positive return rates for investors in the commodity market. Next, the work concentrates on the review of publicly available indices of commodities, leading to the selection of the index that will represent this class of assets most reliably for the requirements of further empirical research. Chapter 2 concludes with a review of the literature related to the occurrence of risk-related bonuses in commodity markets, as well as to the relevance of their application in the case of optimization of an investment portfolio with regard to profit-to-risk relation. The subject matter under consideration in Chapter 3 is active investment in commodity markets, here understood as managed futures investments. As with Chapter 2, the history of the evolution of managed futures funds is discussed first, and it is followed by the characterization of the institutional base of their functioning. Then the basic methods of managing portfolios of managed futures funds are illustrated, together with a theoretical explanation of the occurrence of positive return rates in the case of active investments in commodity markets. Chapter 3 then focuses on the benchmarks of the managed futures branch, and the potential significance of the information that they carry. The purpose of these considerations is to select the relevant benchmark that could serve the requirements of further empirical studies. As in the case of the analysis of passive investments, Chapter 3 concludes with a review of the literature concerning the relevance of investments in the managed futures markets. Chapter 4 focuses on the phenomenon of financialization of commodity markets, that is, on the increasing importance of financial investors in commodity markets. Chapter 4 has two goals: first, to characterize and investigate the influence of the consequences of financialization on the conditions for investment in commodity markets. Five potential effects of the financialization phenomenon have been taken into account: formation of price bubbles, increase of correlation between particular commodities, increase of correlation between the commodity market and the equity market, change of market term structure, and decline in the efficiency of technical analysis, whereby the analysis takes the form of a review of the existing literature. The other goal is an attempt to quantify the significance of three phenomena that could have an essential influence on the conditions for investment in commodities: increase of correlation between the commodity market and the equity market, change of market term structure, and decline in the efficiency of technical analyses. Chapter 5 includes the presentation of tools for evaluating investments in commodity markets. Active and passive deposits on commodity markets,

xviii

Introduction

due to their character—which is essentially different from traditional investments in shares and bonds—require the application of nonstandard investment evaluation measures. Chapter 5 includes two parts. The first part characterizes the investment evaluation measures functioning in existing literature, with an indication of their defects related to the specific character of the commodity market. This is followed by a proposal of two new measures, which will be applied in the empirical analysis. The second part discusses the methodology of research concerning the statistical relevance of including commodities in the traditional portfolio, with shares and bonds. The sixth chapter of the book contains empirical research intended to verify the relevance of investment in commodity markets in two aspects: as an individual investment and as an element of a larger investment portfolio. The examination of the relevance of investment in commodities was conducted with the application of “raw” historical data, as well as after a modification of such data, intended to consider potential structural changes of commodity markets resulting from the increase of significance of financial investors. The book ends with the Conclusion, where the findings of the conducted research are summarized and the question of the relevance of investment in commodity markets is answered. Furthermore, there is an indication of directions for further research, which could potentially throw new light on the issues constituting the subject matter of this book.

Chapter One Asset Allocation in Commodity Markets The focus of this book is strategic asset allocation in commodity markets in relation to the phenomenon of financialization, which is the growing importance of financial investors to these markets. Terms such as asset class, strategic allocation, and even commodities, are not unambiguously understood in existing literature on the subject. The aim of this chapter is to clarify the key issues discussed in the book. In the first place, we will discuss and understand the classes of investment assets; next, we will define strategic asset allocation; and finally, we will propose methods aimed at gaining exposure to commodity markets and examine their importance to this study.

Classes of the Investment Assets There is no strict definition of asset classes, nor is there any closed list of these assets (Idzorek 2007). Asset class can be understood very broadly or very narrowly, depending on the chosen convention and needs. A general definition suggested by Greer (1997) is, “An asset class is a set of assets that bear some fundamental economic similarities to each other, and have characteristics that make them distinct from other assets that are not part of that class.” The concept of asset classes is important to financial market practitioners for several reasons, as indicated by, for example, Scherer and He (2008). First, strictly defined investment asset classes make decision making in the top-down1 processes easier. Considering the fact that asset classes share the same characteristics, they should respond to changes in external factors and the impact of economic forces in the same way. Second, definitions of asset classes streamline and facilitate the construction of a portfolio with various optimization models, the operation of which is based on a risk-return relationship. This is because most of the optimization models react to significant changes in the optimal weight of each asset, in response to small changes in the characteristics of those assets. In other words, optimization models tend to potentiate the errors in estimates in portfolio optimization. For example, if two instruments, X and Y, have a risk and correlation almost equal to that of other instruments but instrument X has a somewhat higher expected return, the optimization model will usually allocate to it a significantly larger

2

The Financialization of Commodity Markets

part of the portfolio, despite the fact that X and Y are very similar to each other. Both instruments are treated by the model as substitutes even though, by definition, different classes of assets are not substitutes. The third reason is related to the specifics of the operation of the investment industry, where there is always an ongoing search for new asset classes. On the one hand, new asset classes hold the promise of greater diversification for the clients of investment firms, while on the other hand, they give investment fund managers the opportunity to offer new products. In summary, the definitions of asset classes not only streamline and simplify the management process, but also have a measurable impact on business processes in the funds industry. The catalog of asset classes divided into the most capacious groups was suggested by Greer (1997). He defined three super asset classes: capital assets, assets that are used as inputs to creating economic value, and store of value assets. Capital assets are defined by their related right to utilize future cash flows. The value of capital assets is assessed by the present value of their future cash flows to the owner. According to the Modigliani–Miller theorem, the value of a firm is unaffected by how that firm is financed, but it is affected by future cash flows.2 The way they are distributed among the shareholders and creditors is a secondary issue, which is irrelevant to the value of a firm (Modigliani and Miller 1958). Thus, stocks and bonds are typical examples of capital assets. In addition to the above, this superclass also includes hedge funds, private equity funds, and credit derivatives (Anson 2009). Assets used as inputs to creating economic value are those that are consumed or converted to other assets in the production cycle. This superclass includes, above all, the so-called physical resources: grains, metals, energy raw materials, and livestock. Assets used as inputs for creating economic value cannot be valuated based on the present value of future cash flows, because they do not generate cash flows. A good example of assets used as a store of value is art (Anson 2009). A picture cannot be transformed in the production process, nor will it be followed by any future cash flow. Its monetary value can be seen only through a sale—or some other form—of transfer of ownership. In addition to art, the class of assets used as a store of value typically includes gold and other ores, although it is worth mentioning here that the border between the asset classes is quite fluid, because ores can also be used in the production process. Other authors adopt narrower definitions of asset classes that are, at the same time, more consistent with common practice. Anson (2009) pointed out that, historically, four asset classes were distinguished: stocks, bonds, cash, and real estate, which were further divided into subclasses such as large companies, small companies, and foreign companies, for example. Anson has suggested extending this division by five alternative asset classes: commodities, hedge funds, private equity funds, futures funds, and

Asset Allocation in Commodity Markets

3

credit derivatives. Sometimes, assets are classified based on risk factors or exposure to risk; these are credit spreads and interest rates (Idzorek 2007). The method of defining and testing the distinctness of the asset class is a complex topic in existing literature. Market practitioners usually refer to the low correlation with other instruments on the market. However, this definition seems to be insufficient. If it was, even a draw ticket bought in a lottery could be defined as a separate asset class because, by assumption, it has zero correlation with the behavior of the stock and bond markets; although, as a rule, no reasonable investor should invest even a fraction of his assets into draw tickets. A formal interpretation of the class of assets is as follows (Greer 1997): each potentially distinct asset class i with return of Ri bearing a risk premium above the return on money market c, which cannot be explained by other j = 1, 2, . . . , J existing asset classes bearing a risk premium Rj−c can be perceived as a distinct asset class. A formal interpretation of an asset class is offered by Greer (1997). According to this author, each potentially distinct asset class with excess returns over risk-free rate, which cannot be explained by excess returns on other existing asset classes, can be perceived as a truly distinct asset class. By illustrating this theorem with an example given by Scherer and He (2008), we should assume that there is a potentially distinct asset class that consists of 25 percent of stocks, 25 percent of bonds, and 50 percent of cash; its annual standard deviation is 30 percent and its expected risk premium is 1.5 percent. In the same market, there are stocks and bonds, the expected returns on which are 5 percent and 2 percent respectively, with volatility being 15 percent and 5 percent respectively. Considering the content of a new asset class, it can be calculated that its correlation with stocks is 0.24. Nevertheless, despite the low correlation with other asset classes and the positive risk premium, we cannot conclude that this instrument constitutes a distinct asset class. A typical optimization model would not allocate in it, because a portfolio with better profit, risk values, and Pareto parameters than those of the possible new asset class being discussed can be constructed based on the instruments already existing in the market. All investment portfolios that can be created with the “new” asset class can also be constructed with existing instruments. The growing investment universe does not move the efficient frontier upward, nor to the left, as the asset class does not generate a return directly related to its specific risk. If there was such a premium, and if the efficient frontier was significantly moved upward and to the left, then we could speak of a distinct investment asset class allowing the construction of a portfolio with better parameters in terms of its riskreturn relationship. The empirical study in the latter part of this book bases on the four asset classes. Two of them are a reference point for the traditional investor, whose investment portfolio usually consists of stocks and bonds. The other two

4

The Financialization of Commodity Markets

are placed under analysis. These are the passive investment strategies implemented in commodity markets—for which a relevant benchmark tracking its nature is based on the commodity market indices—and the active investment strategies in commodity markets—for which managed futures are a representative measure. Such an approach is widely accepted and has been used in many studies (Lintner 1983; Scherer and He 2008; Anson 2009; CISDM 2009, etc.).

Strategic and Tactical Asset Allocation In financial literature, there are three types of asset allocation: strategic asset allocation, tactical asset allocation, and benchmark asset allocation.3 In any case, the aim is to construct a portfolio with possibly the best riskreturn relationship profile (Fuss, Kaiser, Rehkugler, and Butina 2006). Strategic asset allocation is based on long-term—usually at least fiveyear—forecasts for each asset class. Long-term forecasts, by nature, are not normally updated, so strategic allocation is characterized by weights that are stable over long term. Strategic allocation can be expressed as a specific percentage of the portfolio (e.g., 25 percent), but it is often formulated also as a range within which some variations are acceptable (e.g. 20–30 percent). Tactical asset allocation is based on short-term deviations from the strategic objectives, acceptable in order to take advantage of the current market situation. Tactical allocation is usually much more dynamic, as compared to the strategic one, and the investment horizon associated with the decisionmaking usually ranges from one month to three months. As for benchmark allocation, the manager invests the resources according to the weights defined in the established benchmark. This approach applies, for example, to index funds and funds adopting quasi-index strategies, such as enhanced-indexing (Ruh 2001; Kommer 2007; Magers 2007). Benchmark allocation is done with the a priori defined asset weights within a given benchmark, and it does not require any forecast from the fund manager. Strategic allocation relies mostly upon historical data (average rate of return, standard deviation) as a basis for the forecasts of the next five years. Such an approach is dangerous because it implies that no other information is relevant to the strategic allocation. On the other hand, tactical allocation is the result of active and dynamic forecasts, made by a fund manager, based on various premises. The study in the last part of the book focuses on the issue of strategic asset allocation. In this author’s opinion, it is possible that the structural changes that have occurred in recent years in the commodity markets might have a significant impact on the premises for formulating the strategic asset allocation. One of the potential consequences of financialization of the commodity markets may be the fact that including commodities into the process of strategic allocation is currently unfounded.

Asset Allocation in Commodity Markets

5

Investments in Commodity Markets The asset class and the way to gain exposure to this class have to be distinguished between when selecting investment assets. For example, in case of stocks, the exposition to them can be gained, inter alia, through the direct purchase of stocks, through acquisition of participation units in an equity fund, or by adopting a long position in index futures. Unlike most asset classes for which exposure can be achieved in a relatively clear and simple manner, the situation is quite different in the case of commodities. There are three basic ways to obtain exposure to the market of commodities, each of which results in different profiles of risk and revenue (Idzorek 2007): ●● ●● ●●

direct purchase of physical commodities, portfolio of companies related with commodities, commodity futures.

Investments in physical commodities are not widely used (Idzorek 2007). Most of the commodities are consumed and decaying, particularly, agricultural commodities and livestock. The only exceptions were ores; thus, apart from funds investing in gold or silver, for example, there are only a few funds and investors allocating their resources through direct purchase of commodities. Using commodity companies as a proxy for commodities as an asset class is a highly controversial issue. This investment gives exposure to the skills and competencies of the boards of management of companies and their business lines, and, in fact, it is a subclass of a broader class of assets; namely, the stocks rather than the commodities themselves. What’s more, many companies may hedge their positions in the commodity markets with hedging strategies. As a result, the commodity companies are usually much more correlated with the stock market than with the commodities market (Zaremba 2011d). Some interesting research on this subject has been accomplished by Gorton and Rouwenhorst (2006). The authors had built up a portfolio of commodity companies according to the SIC codes, and studied its behavior for 41 years. The correlation of the portfolio with the index of commodity futures was 0.4, and its correlation with the stock market was 0.57. In other words, the behavior of commodity companies is more similar to companies on a stock market than those in a commodities market. What’s more, historically, the commodity companies bore lower rates of return than commodity futures; however, it is not definitely demonstrated that they effectively hedge against inflation. As a result, we cannot assume that the portfolio of commodity companies is a good way to gain exposure to the commodities market (Baierl, Cummisford, and Riepe 1999; Chen and Pinsky 2003; Idzorek 2007). The third method to gain exposure to the commodities market is by investing through futures contracts. In this case, there are two ways to gain exposure to commodity futures markets: “passive investment strategies”

6

The Financialization of Commodity Markets

through a basket of commodities that compose the index or the sub-index; or “active investment strategies” through managed futures.4 The index investment consists in opening a position in a basket of futures that is composed of an index or sub-index, and, then, they are systematically rolled at the time of expiration of each contract. Such a basket is fully hedged with secure instruments—most often with treasury bonds—and the gained returns are associated with a few different sources of profit, including changing prices in the spot market, profit from rolling positions, and interest from a margin deposit. The literature on the subject points to a number of properties of investments in commodities with indices of commodity markets, which are attractive from the investor’s perspective. We should mention here the rightskewedness of the distribution of return rates (Deaton and Laroque 1992; Armstead and Venkatraman 2007); regression of rates of return toward the mean (Sorensen 2002); positive correlation with inflation (Bodie 1983; Froot 1995; Till and Eagleeye 2003a,b; Gorton and Rouwenhorst 2006; Akey 2007); positive correlation with economic activity (Strongin and Petch 1995; Strongin and Petch 1996; Gorton and Rouwenhorst 2006; Armstead and Venkatraman 2007; Kat and Oomen 2007a,b; Adams, Fuss, and Kaiser 2008); long-term positive risk premium (Till 2007a, b,c); and incomplete correlation with traditional asset classes, useful in the process of portfolio optimization (Ankrim and Hensel 1993; Becker and Finnerty 1994; Kaplan and Lummer 1998; Anson 1998; Abanomey and Mathur 2001; Georgiev 2001; Gorton and Rouwenhorst 2006). For this study, the last two properties mentioned are particularly important. The second method to gain exposure to the futures market is by investing through managed futures portfolios. These funds operate based on the activities of traders, known as Commodity Trading Advisors (CTAs). Initially, CTAs made transactions for themselves directly on the futures markets in the United States, according to their own strategies. Since then, their skills have been recognized by financial institutions that have used their competencies on a much larger scale. Today, the term “Commodity Trading Advisors” is sometimes used interchangeably with the term “managed futures,” although their meanings are not completely identical. The method of operation of managed future funds involves the active opening and closing of long and short positions in different commodities markets, in accordance with the previously adopted strategy. Due to their specifics, the investments in managed future funds have many features, which make them an attractive addition to an investment portfolio. The literature on the subject usually mentions high rates of return with limited risk (Edwards and Park 1996), the positive effect of portfolio diversification (CISDM 2006), and improved performance of the investment portfolio when usefulness of traditional and alternative asset classes is limited (CISDM 2006). In recent years, many studies and analyses, which seem to confirm this hypothesis to some extent, have been carried out (McCarthy, Schneeweis, and Spurgin 1996; Edwards and Park 1996; Schneeweis,

Asset Allocation in Commodity Markets

7

Spurgin, and Potter 1997; Schneeweis and Spurgin 1997; Schneeweis 2000; CISDM 2006). Due to the diverse nature of the two types of investments, this study aims to describe the two mentioned methods of gaining exposure to the raw materials market separately, and later. What we will discuss first are the passive investment strategies based on a raw material market index, and then we will explain active investment strategies through the managed futures portfolios.

Chapter Two Passive Investment Strategies in Commodity Markets This chapter is devoted to passive investments in commodity markets with a portfolio of futures contracts. The aim of this chapter is to provide a detailed discussion of how investments in futures contracts in the commodity markets operate, with particular emphasis on the existing sources of profit and the methods of their exploitation by the investor, using different baskets of futures that comprise the commodity indices. A thorough understanding of sources of profit in the commodity markets of futures is necessary for further analysis of the potential impact of the financialization of the commodity markets on the validity of investing capital in these markets. The chapter consists of several sections. We start with the brief history of commodity markets before we go on to discuss the various sources of profit that determine rates of return in the commodity markets. The theoretical justification for the existence of long-term positive returns in commodity futures markets and the explanation of the formation of futures curves are the subject of the next section of the material. Then we review and discuss the rules of establishing commodity market benchmarks, with the intention of selecting a benchmark best representing this asset class—from the investor’s point of view. The chapter ends with a critical review of the existing literature on the validity of investing capital in the commodity markets.

The Genesis of Investments in the Futures Market Although futures contracts are now associated mainly with electronic trading and advanced technology, nonetheless, in spite of appearances, they are not a novelty in the financial market. There is no consensus in existing literature on the beginning of the futures market; some authors date it back to antiquity. One of the first transactions that bears the hallmarks of a futures contract can be found in Politics by Aristotle. The author tells the story of Thales, a poor philosopher from Miletus, who used his skills to forecast an abundance of olives in the coming fall. Being sure of his own predictions, Thales entered into contracts with local producers of olives. He prepaid an appropriate collateral and acquired the right to the exclusive use of olive

10

The Financialization of Commodity Markets

presses during the harvest season. Thales successfully negotiated low fees because, at the moment of conclusion of the contract, nobody knew whether the harvest would be successful or not, and the producers wanted to protect themselves against the risk of bad crops. When in the autumn, a time of harvest that proved to be extremely abundant had come about, and the demand for the use of olive presses heavily outstripped the supply, Thales sold his rights at much higher prices. Thanks to the accurate forecast and skillful transactions, the poor philosopher earned a lot of money. The origins of the futures markets are also dated to the age of Tulip mania in the Netherlands (Dash 2001); nonetheless, the first organized futures market was most probably born in Japan. We mean here the Dojima rice exchange (jap. Dōjima kome kaisho), on which the trading with futures contracts has been carried out since 1710 (West 2000). Despite the Asian and European roots that have already been listed, the origins of the futures in their current form should be seen in the grain markets of the United States, especially in Chicago—the most important point on the map of history of the futures markets. In the early nineteenth century, the vast majority of American farmers produced grain mainly for their own use, to feed their families. However, by as early as the 1850s, the development of large area production led to the growth of trade, infrastructure, warehouses, and railways, and to the increased scale of activity of the merchants. As a result, new forms of financing were looked into, which, after a transitional period of bankers’ acceptances (Santos 2008), led to the birth of the forward contract (not the futures).1 The literature indicates that the first futures contract in the United States was concluded on March 13, 1851. Soon after that, a grain categorization, standardization, inspection, and storage system was established. These factors were positively influencing the development of the commodity markets (Chandler 1977, p. 211). One of the largest and the most prominent was to be the Chicago Board of Trade, established in 1848 (Lurie 1979, p. 27). An important date in the development of futures markets is March 27, 1863. That was the day when the Chicago Board of Trade (CBOT) adopted the first rules and procedures for the settlement of futures contracts (Hieronymus 1977, p. 76). It was an extremely important issue to the operation of the market, because it was usually easy to find a trader prone to take a position in a futures contract, but it was very difficult to find an investor willing to participate in the final settlement of the transaction. It is worth mentioning that back then, the contracts were usually settled with a delivery not with money (Santos 2008).2 May 1865 is considered the beginning of the organized futures market, but it is difficult to pinpoint the exact date. It was a period during which the CBOT standardized contracts specifications, defined collateral requirements, set out the procedures related to settlement and supply of contracts, and restricted trade in contracts to the exchange members (Hieronymus 1977, p. 76). In other words, in May 1865, the forward contracts were transformed into the futures contracts (Santos 2008).

Passive Investment Strategies in Commodity Markets

11

140 120

Futures volume (2002: CBT, excludes Barley and Rye) Crop (year marketed)

100 80 60 40 20 0 1884–1888

Figure 2.1 States.

1889–1893

1894–1898

1966–1970

2002

Production of grain and trading with grain futures in the United

Source: Santos (2008).

Santos (2008) attempted to answer the question of how trade in the early years of the futures markets looked. Figure 2.1 shows a summary. In addition to trading volume, we should pay attention to the relationship between the volumes of trade and production. From the very beginning, that is, from the 1880s onward, market turnover exceeded the production volume eightfold. In the following years, this rate gradually shrank and in the early twentieth century, it increased again—up to about 11 times (Santos 2008). Figure 2.2 presents a similar sheet regarding the Cotton Exchange in New York. Again, it is worth noting that only in 1871 did cotton production actually exceed the turnover. Since then, the market liquidity improved regularly; in 1879, it grew fivefold and 16 years later, the trading volume outstripped the supply from the producers eight times over. By the end of the 1970s, futures contracts were inseparably related to the commodity market. This bore consequences to market nomenclature itself; after all, chartered futures market advisors in the United States, even today, are called Commodity Trading Advisors, although their mandate is not limited to commodity markets. For financial instruments, a breakthrough year was 1971. Until this year, global currencies were strongly backed by gold. Deregulation of the exchange rates in 1971 allowed the quotations to drift freely. The authorities of the Chicago Mercantile Exchange (CME) realized then that the exchange rates, pushed by the market forces, had become a typical commodity. A natural consequence was the demand shown by entrepreneurs, who wanted to minimize the risk of currency fluctuations. Although there was already a sizable market in forward contracts, there was no market for publicly listed futures contracts. In 1971, the CME established the

The Financialization of Commodity Markets

12 140,000

Futures volume (2002: 1 bale=480lbs.)

120,000

Cotton crop (year marketed) 100,000 80,000 60,000 40,000 20,000 0 1871

1874

1877

1880

1883

1886

1889

1892

1895

2002

Figure 2.2 Cotton production and trading with futures contracts in the New York Cotton Exchange from 1871 to 1897, 2002. Source: Santos (2008).

International Monetary Market (IMM), in which the new instruments were sold. Another innovative idea was the creation of contracts based on interest rates. In 1976, the CME introduced the first product of this kind, the futures based on 90-day US Treasury bill. Within six years later, it became the most-traded instrument listed in the CME. The next step was to offer futures on interest rates; that is, the so-called eurodollar contracts. This instrument ushered in a new era of products that could only be settled financially, because the physical delivery of the underlying instrument is simply impossible. Futures contracts on indices and stocks are relatively the youngest in the family. The first contract on the S&P 500 index was introduced by the CME as late as in 1982; however, the investors discovered very quickly that instruments based on the stock market could be very useful to protect themselves from unwanted fluctuations in prices, so the contracts on indices quickly gained much popularity. Currently, financial futures contracts significantly exceed their commodity predecessors. 1982 was the first year when the volume of contracts on agricultural products fell below 50 percent of the world turnover, and as early as in 1985, it constituted only a fourth of this value. Interestingly, at exactly the same time, the futures on American bonds alone overtook all agricultural commodities combined (Leuthold, Junkus, Cordier 1989, p. 2). Currently, financial futures contracts, and particularly those based on public instruments, are the leaders in terms of market statistics. Table 2.1 shows the volumes of transactions in various categories of futures and options in 2013.

Passive Investment Strategies in Commodity Markets

13

Table 2.1 Global volume of trading with options and futures contracts in the world in 2013 broken down into the underlying instruments Category

Volume

Share in total volume (%)

Individual equity

6,401,526,238.00

29.6

Equity index

5,370,863,386.00

24.8

Interest rates

3,330,719,902.00

15.4

Currency

2,491,136,321.00

11.5

Energy

1,265,568,992.00

5.8

Agriculture

1,213,244,969.00

5.6

646,318,570.00

3.0

Non-Precious metals Other

493,359,639.00

2.3

Precious metals

430,681,757.00

2.0

21,643,419,774.00

100.0

Total

Source: Author’s elaboration based on Acworth, Futures Industry Association (2013).

Table 2.2 Global volume of trading with options and futures contracts in the world in 2013 by regions Region

Volume

Share in global volume (%)

North America

7,940,222,591

36.7

Asia

7,291,409,895

33.7

Europe

4,351,305,986

20.1

Latin America

1,683,076,253

7.8

Other Razem

377,405,049

1.7

21,643,419,774

100.0

Note: Based on the number of contracts traded and/or cleared at 84 exchanges worldwide. Location of exchanges is determined by country of registration. Other consists of exchanges in Dubai, Israel, South Africa, and Turkey. Source: Author’s elaboration based on Acworth, Futures Industry Association (2013).

In addition, in recent years, the importance of the United States in the futures markets is systematically decreasing. The volume of the turnover, broken down by regions of the world, is shown in table 2.2, whereas table 2.3 shows the scale of trading on the ten most active futures markets in the world. Besides product and geographical changes in the markets of derivatives, futures contracts and options trading volume are constantly growing; only between the years of 1998 and 2013, the number of transactions increased eightfold. A summary of the trade in the mentioned period is shown in figure 2.3.

The Financialization of Commodity Markets

14

Table 2.3 The world’s largest markets of futures and options in terms of trading volume in 2013 Rank

Exchange

Volume

1

CME Group

3,161,476,638

2

Intercontinental exchange

2,807,970,132

3

Eurex

2,190,548,148

4

National Stock Exchange of India

2,135,637,457

5

BM&FBovespa

1,603,600,651

6

CBOE Holdings

1,187,642,669

7

Nasdaq OMX

1,142,955,206

8

Moscow Exchange

1,134,477,258

9

Korea Exchange

820,664,621

10

Multi Commodity Exchange of India

794,001,650

Source: Author’s elaboration based on Acworth, Futures Industry Association (2013).

30,000 25,000 20,000 15,000 10,000 5,000

19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13

0

Figure 2.3 Global volume of trading with futures and options in the years 1998– 2013 (million units). Source: Acworth, Futures Industry Association, (2009, 2011, 2012, 2013, 2014).

Sources of Profit from Passive Investment Strategies in Commodity Markets One of the advantages of the commodity indices as a form of investment is that they derive from a variety of sources of profit that often are not correlated with each other. Spurgin and Donohue (2009) list a large number of

Passive Investment Strategies in Commodity Markets

15

concepts present in the literature and describe sources of profit related to investment in the commodities. The sources of profit related to investments in futures contracts in the commodity market can be divided into two categories. The first group concerns the behavior of futures contracts. They include spot yield or spot returns, collateral yield or collateral returns, and roll yield or roll returns. The second group is associated with the design of the index. We can include here such elements, identified by Spurgin and Donohue (2009), as diversification returns, returns associated with weighting method, returns associated with dynamic asset allocation, and returns associated with specific contract maturity date.

Spot Return A spot return is a change in quotation of a futures contract. In practice, it is the excess return lowered by profit or loss upon rolling the position. A number of published studies show historical calculations of profits from investments in futures contracts with individual components, including spot returns (Markert and Zimmermann 2008; Fuss, Hoppe and Kaiser 2008; Hafnem and Heiden 2008; Mezger 2008; Shore 2008). Interestingly, the very first studies showed that long-term spot returns are close to zero (Grili and Yang 1988). In other words, historically, investment in commodities in pure form proved not to bring statistically significant, positive, or real (adjusted for inflation) rates of return; rather, the profits were rather related to other mechanisms associated with the futures markets and the design of an investment portfolio. Similar results have been achieved later by Cuddington (1992), Cashin, McDermott, and Scott (1999), Cashin and McDermott (2001), and Burkart (2006). Interesting conclusions have been drawn by Anson (1998), who has pointed out that although—in the case of commodities—other factors (roll returns, collateral interest) are responsible for long-term return rates, the spot price change is the factor that affects the diversification capabilities of the commodities in the design of an investment portfolio. The issue of long-term yields in the spot market was also analyzed by Gorton and Rouwenhorst (2006). The results of their research concerning the commodity portfolios are shown in figure 2.4. As the research by Gorton and Rouwenhorst (2006) shows, only the systematically rebalanced commodity portfolios in the spot market allowed investors to generate higher geometric rates of return than inflation, in the long term. A simple “buy and hold” strategy led to a loss in real value of the analyzed portfolio, in the period from 1957 to 2004. Similar conclusions have been drawn by Erb and Harvey (2006a), who analyzed both the quotations of the GSCI index and of each commodity index between 1982 and 2004. In most cases, the analyzed investments did

The Financialization of Commodity Markets

16 9 8

8.42 7.66

Arithmetic average Geometric average

7.51 6.66

7 6

4.64

%

5

4.14 4.13 3.47

4 3 2 1 0 Monthly rebalancing

Yearly rebalancing

Buy-and-hold

Inflation rate

Figure 2.4 Average annualized returns to spot commodities and collateralized commodity futures from 1959 to 2004–2012. Source: Author’s elaboration based on Gorton and Rouwenhorst (2006).

not produce any statistically positive income. Detailed results of the research by Erb and Harvey are presented in table 2.4. Upon analyzing the presented studies, it is hard to accept the notion that changes in the spot prices are the most important source of profit in the commodity market. Although the investors generally understand that it is the growth in a spot quotation that is the key for generating profits in commodity markets, however, the hitherto obtained research does not confirm clearly that the real spot rates (adjusted for inflation) are positive. As a result, in this author’s opinion, when analyzing the impact of financialization on the validity of an investment in the commodity market, special emphasis should be placed on other sources of profit.

Roll Return Profit and loss on roll (roll yield or roll return) are related to the expiry of the position in the futures contract at the time of its maturity and the opening of a new position in the next series. Roll return can be positive when the forward curve is downward sloping or negative when it is rising. Some commodity markets have a variable forward curve while, in the case of other markets, one shape is clearly dominant. An example may be the gold market, for which most of the time the forward curve is in contango.3 In the calculation of rates of return with respect to division into total return, spot return, and excess return, the roll is responsible for the difference between excess profit and the spot profit. What’s more, most authors of analyses of sources of profit agree that the roll return is the

2.45 –3.13 4.00 –5.42

Livestock

Agriculture

Industrial metals

Precious metals

1.92 3.69

5.07 –2.75 –5.39 –5.63 –0.35 –3.12 –6.36

Live hogs

Wheat

Corn

Soybeans

Sugar

Coffee

0.85

–3.32

–3.32

0.17

5.94

5.53

Live cattle

10.51

–4.46

6.41

–2.15

3.48

11.52

0.36

5.81%

Arithmetic mean

Heating oil

Commodities

7.06

–0.12

4.49%

Geometric mean

Energy

Nonenergy

Sectors

GSCI

Index/Sector/Commodity/ Portfolio

39.69

38.65

21.49

22.65

21.05

24.21

13.98

32.55

14.88

22.82

14.35

14.51

31.23

9.87

16.97%

Standard deviation

–0.74

–0.37

–0.08

–1.15

–1.18

–0.53

1.68

0.79

–1.69

0.81

–1.01

0.78

1.05

–0.06

1.22

t-Statistic

Table 2.4 Historical excess returns, December 1982–May 2004

1.12

1.60

0.44

1.37

0.16

–0.04

–0.51

0.64

0.29

1.27

0.20

–0.19

0.73

0.09

0.51

Skewness

3.09

7.03

1.86

9.16

0.17

1.14

2.74

1.94

2.21

5.92

0.85

0.93

2.28

–0.01

1.98

Kurtosis

–0.16

–0.08

–0.02

–0.25

–0.26

–0.11

0.36

0.17

–0.36

0.18

–0.22

0.17

0.23

–0.01

0.26

Sharpe ratio

0.01

0.03

0.01

0.00

–0.01

–0.04

0.02

0.04

–0.18

0.06

–0.01

0.05

0.15

0.01

0.11

Autocorrelation

continued

No

No

No

No

No

Yes

Yes

Yes

Difficult storage

–8.09 6.17

Silver

Copper

–1.71 3.45 7.35 5.84

EW rebalanced

Average of 12 commodities

Lehman Aggregate

S&P500

EAFE

7.18

8.30

3.50

1.51

1.51

1.26

9.15

–5.30

–4.81

2.60

Arithmetic mean

17.29

15.30

4.65

25.16

10.05

10.61

25.69

25.03

14.36

22.64

Standard deviation

Source: Author’s elaboration based on Erb and Harvey (2006), p. 74.

0.70 1.01

Initially EW, buy-and-hold

Portfolios

0.10 –5.68

Gold

Geometric mean

Cotton

Index/Sector/Commodity/ Portfolio

Table 2.4  Continued

1.56

2.22

3.43

–0.31

0.46

0.31

1.11

–1.49

–1.83

0.02

t-Statistic

–0.22

–0.76

–0.20

0.60

0.01

0.05

1.03

0.46

0.30

0.61

Skewness

0.38

2.70

0.48

3.06

0.37

0.69

3.92

2.05

2.33

1.37

Kurtosis

0.34

0.48

0.74

–0.07

0.10

0.07

0.24

–0.32

–0.40

0.00

Sharpe ratio

0.05

–0.01

0.12

–0.01

–0.04

0.01

0.06

–0.15

–0.14

0.05

Autocorrelation

Yes

No

No

No

Difficult storage

Passive Investment Strategies in Commodity Markets 8

R2 = 0.9235

Copper

6

Cattle

4 Excess return (%)

19

Heating oil

2 Soybeans –8

Corn

–6

–4

Wheat Gold Coffee Silver

0

–2 Hogs Sugar

Cotton

0

2

4

6

–2 –4 –6 –8 –10 Roll return (%)

Figure 2.5 Commodity excess returns and roll returns, December 1982–May 2004. Note: Compound annualized excess return: intercept = 0.89 percent; intercept t-statistic = 1.84; roll coefficient = 1.20; roll t-statistic = 10.97; adjusted R2 = 91.57 percent. Source: Author’s elaboration based on Table 6 from Erb & Harvey (2006a), p. 80.

key component in determining the long-term rate of return on each commodity market (Nash 2001; Till and Eagleeye 2003a; Kat and Oomen 2007a,b). This is shown quite clearly in the simple analysis below, which is based on data from a paper by Erb and Harvey (2006a, p. 80), presented in figure 2.5. The analysis carried out shows that, in the past 20 years, roll returns have probably been the key factor affecting the excess return rates (in addition to interest on collateral) in commodity markets. The coefficient of determination of excess return by the roll return was 92.35 percent. In addition, as will be pointed out in the following sections of the text, the roll return, for a long time, was a very important component of rates of return in the commodity market.4 This was, in fact, confirmed by Markert and Zimmermann (2008). Their regression yielded an R-squared of 88.6 percent.

Collateral Yield The total return indices assume that the positions are fully hedged (usually by investing in treasury bonds), which means that collateral yield or collateral return increases the rate of return on the indices. It is interesting to note that, historically, collateral returns proved to be a very important source of profits. In cases of indices with a long track record (e.g., S&P GSCI), most profits can still be attributed to collateral yield, which is, first

The Financialization of Commodity Markets

20

and foremost, due to high interest rates in the 1970s and 1980s. However, this component is, of course, not responsible for the risk premium in the commodity market, so it is not a key factor from the investor’s point of view. In other words, it can easily be replicated by means of investments in bonds or treasury bills.

Diversification A low degree of correlation between prices of different commodities, on the one hand, has the effect of lower volatility throughout the index and, on the other hand, is associated with the so-called diversification return. The term “diversification return” was coined by Booth and Fama (1992) (it was mentioned earlier by Fernholz and Shaw (1982) as well as later, by Luenberger (1998)). According to Booth and Fama, under the defined conditions, the diversification return may significantly increase the average geometric rate of return from systematically weighted and rebalanced5 portfolios of commodity futures. Willenbrock (2011) more accurately interpreted this return as the return on rebalancing. The effects of this phenomenon are not used by the portfolios that are not rebalanced, such as the indices weighed by the market value of the assets (Erb and Harvey 2006b). According to Booth and Fama, it can be accepted as a golden rule that the geometric average rate of return on rebalanced portfolios is higher than the geometric average rate of return on “let-itrun” portfolios. Campbell (2000), in his analysis, calls the diversification return “the only free lunch in finance,” because it allows, at the same time, raising the rate of return and reducing the risk; Erb and Harvey (2006a) point out that many authors, including Gorton and Rouwenhorst (2006) in their most famous article, confuse the diversification return with the risk premium. The diversification return essentially consists essentially of two components. The first one results from the reduction in variance; the long-term average of geometric rates of return can be approximated by equation (1) (Francis 2000; de la Grandville 1998): rg = ra −

δ2 2

(1)

whereby rg represents the geometric average of rates of return; ra is the arithmetic average of the rates of return; and δ is the standard deviation of rates of return during the analyzed period. As a result, reduction in variance of the rebalanced portfolio, which is the result of greater diversification, will lead to an increased geometric average on the rates of return. The second component of the diversification return is the covariance between the weights of the individual instruments in the portfolio and their past rates of return (Erb and Harvey 2006a). In the absence of rebalancing,

Passive Investment Strategies in Commodity Markets

21

this component will be negative, since the increased share of instruments, which, in the past, have been growing in their value to the portfolio, will reduce the benefits from the first component. According to Erb and Harvey (2006a), the diversification return for the equal-weighted portfolio can be approximated by equation (2): rd =

1 1  1 −  δ 2 K

2

(1 − p )

(2)

whereby rd represents the diversification return; K is the number of instru2 ments in the portfolio; and δ and p represent average variance and correlation in the portfolio, respectively. Upon analyzing the equation presented above, it is easy to see that the gains from diversification will be greater. The more instruments that there are in the portfolio, the smaller will be the correlation between them and the higher will be their volatility. Table 2.5 presents the calculated return on portfolio diversification for different levels of the above-mentioned parameters. It is worth seeing this table through the prism of the existing studies on the long-term rates of return on commodity indices. Gorton and Rouwenhorst (2006), for example, have documented in their sample an excess return of 4.52 p.p. Whereas, in respect of the fact that the standard deviations of rates of return on contracts in the analyzed sample reached nearly up to 30 percent, then, depending on the level of correlation between the analyzed markets, the diversification return could be 3–4.5 p.p. Bodie and Rosansky (1980), in turn, recorded a long-term geometric average of the rates of return on a commodity portfolio amounting to 8.52 percent. Considering the fact that, when analyzing them, the average volatility stood at 40 percent and the correlations were made within the limits of 0.1, it is possible that as many as 7 percentage points in the above-mentioned rate of return resulted from diversification. The crucial importance of diversification to averages of long-term rates of return on the commodity market has also been emphasized by other authors: Scherer and He (2008), for example. De Chiara and Raab determined a 2.8 percent diversification return on the rebalanced Dow Jones AIG portfolio in the years 1991–2001, and Plaxco and Arnott (2002) as well as Erb and Harvey (2006a) noted that the rebalanced portfolios tend to have higher Sharpe ratios than the let-it-run portfolios do.

Weighting Method The index weighting method can also become a source of excess return when higher weights are assigned to commodities that, for various reasons, have generated higher returns over the observed period, and lower weights are assigned to those that have generated lower returns. Nonetheless, one should be aware that assigning high weights to a narrow group of goods

The Financialization of Commodity Markets

22

Table 2.5 Simulation of diversification returns for different numbers of instruments in the portfolio and for different volatility and correlation Average correlation

Average standard deviation (%)

Number of instruments in a portfolio (%) 5

10

15

20

25

30

35

40

0.00

10

0.40

0.45

0.47

0.48

0.48

0.48

0.49

0.49

0.20

10

0.32

0.36

0.37

0.38

0.38

0.39

0.39

0.39

0.40

10

0.24

0.27

0.28

0.29

0.29

0.29

0.29

0.29

0.60

10

0.16

0.18

0.19

0.19

0.19

0.19

0.19

0.20

0.80

10

0.08

0.09

0.09

0.10

0.10

0.10

0.10

0.10

0.00

20

1.60

1.80

1.87

1.90

1.92

1.93

1.94

1.95

0.20

20

1.28

1.44

1.49

1.52

1.54

1.55

1.55

1.56

0.40

20

0.96

1.08

1.12

1.14

1.15

1.16

1.17

1.17

0.60

20

0.64

0.72

0.75

0.76

0.77

0.77

0.78

0.78

0.80

20

0.32

0.36

0.37

0.38

0.38

0.39

0.39

0.39

0.00

30

3.60

4.05

4.20

4.28

4.32

4.35

4.37

4.39

0.20

30

2.88

3.24

3.36

3.42

3.46

3.48

3.50

3.51

0.40

30

2.16

2.43

2.52

2.57

2.59

2.61

2.62

2.63

0.60

30

1.44

1.62

1.68

1.71

1.73

1.74

1.75

1.76

0.80

30

0.72

0.81

0.84

0.86

0.86

0.87

0.87

0.88

0.00

40

6.40

7.20

7.47

7.60

7.68

7.73

7.77

7.80

0.20

40

5.12

5.76

5.97

6.08

6.14

6.19

6.22

6.24

0.40

40

3.84

4.32

4.48

4.56

4.61

4.64

4.66

4.68

0.60

40

2.56

2.88

2.99

3.04

3.07

3.09

3.11

3.12

0.80

40

1.28

1.44

1.49

1.52

1.54

1.55

1.55

1.56

0.00

50

10.00

11.25

11.67

11.88

12.00

12.08

0.20

50

8.00

9.00

9.33

9.50

9.60

9.67

9.71

9.75

0.40

50

6.00

6.75

7.00

7.13

7.20

7.25

7.29

7.31

0.60

50

4.00

4.50

4.67

4.75

4.80

4.83

4.86

4.88

0.80

50

2.00

2.25

2.33

2.38

2.40

2.42

2.43

2.44

12.14 12.19

Source: Author’s elaboration.

could reflect in a high rate of volatility of the indices. In practice, the weighing system usually should have some economic justification in order to avoid the charge of optimization based on historical data. The adopted method of weighing may be the key factor when it comes to the determination of rates of return on the index. For example, energy commodities generated historically high rates of return. As a result, the indices with a significant share of these raw materials, like S&P GSCI, may be characterized by higher historical rates of return (Idzorek 2007; Fuss, Hoppe, and Kaiser 2008).

Passive Investment Strategies in Commodity Markets

23

Dynamic Asset Allocation Some indices are characterized by dynamically changing weights of individual commodities. Changes to the weights are determined by different types of models, of which the most popular are based on the behavior of futures prices. Some indices use the momentum principle, which holds that commodities whose prices have recently increased should prevail and the commodities whose prices have recently fallen should be reduced. The reversion indices are characterized by a reverse mechanism. In this case, the positions whose rates have increased should prevail and the positions whose rates have decreased should be reduced. The models based on momentum are usually short-term, while the reversion models are based on the rates of return for periods longer than six months. Probably the best-known index that uses the principle of momentum is the Mount Lucas Management Commodity Index, and the effect itself has been already well documented in the literature on commodity markets (Pirrong 2005; Erb and Harvey 2006a; Miffre and Rallis 2007). Among other indices with the rudimentary features of dynamic allocation in accordance with previously adopted rules (that is, enhanced indexing), are the indices developed by Deutsche Bank. The Deutsche Bank Mean Reversion Commodity Index assigns greater weight to the commodities, which, in the previous period, recorded the biggest loss in their value, and the Deutsche Bank Optimum Yield Index prefers the commodities characterized by the largest backwardation (Lewis 2007).

Maturity of the Contracts The choice of maturity of futures contracts used to gain exposure to the commodity market is also of great importance for the gained rates of return. Traditional and historically, elder commodity futures indices operate in a way shown in figure 2.6. Price

(2) (1)

Maturity

Figure 2.6 Rolling futures contracts in the “elder” generation of indices of the commodity markets. Source: Author’s elaboration.

The Financialization of Commodity Markets

24 Price

(2)

(1)

Maturity

Figure 2.7 Rolling futures contracts in the “younger” generation of indices of the commodity markets. Source: Author’s elaboration.

Strategies, on which the traditional index is based, assume a buying contract with short-term maturity (most often one month), keeping it for a month (1), and then rolling it over to the next contract (2). However, as many studies have shown, futures curves in commodity markets—especially those related to energy raw materials and industrial metals—are often “humped.” This means that they rise initially, and then fall through further terms. As a result, the roll returns are usually higher in the latter part of the curve (UBS 2011). It is illustrated in figure 2.7. The strategy of the “new” generation indices implies the spot purchase of features contracts with a long maturity period and rolling it after a month to another contract with the same maturity as the closed position at the time when it was open. In addition, it is worth noting that contracts with a long period of maturity are usually characterized by lower volatility (UBS 2011). An example of this category of indices is the UBS Constant Maturity Commodity Index. The disadvantage with the indices described above is that they give exposure to the portion of the curve. As a result, they become the object of criticism because, on the one hand, they do not fully reflect the entire asset class—the commodities—but only some part of it. On the other hand, there are arguments made that the “newer” generation indices are being excessively optimized because they only focus on the instruments with the historically highest rates of return. These problems are solved by indices that provide exposure to the entire forward curve at the same time. This technique is illustrated in figure 2.8. Such indices are fitted-in with a built-in mechanism that involves the systematic acquisition and rolling of all futures contracts in the entire forward curve on a monthly basis. The indices of this type provide the most complete exposure to the commodity market. The example of such an index is the JP Morgan Commodity Curve Index, which is designed to assume

Passive Investment Strategies in Commodity Markets

25

(2)

Price

(2)

(2)

(2) (2)

(1)

(2)

(1)

(2) (1)

(1)

(1) (1)

(1)

Maturity

Figure 2.8 Rolling futures contracts for indices that provide exposure to the entire forward curve. Source: Author’s elaboration.

open positions on the entire length of the forward curve in proportion to the number of open positions for each maturity date (JP Morgan 2007). In effect, it gives a complete picture of the results generated by all participants with long positions in commodity markets. At this point, it is worth noting that different techniques of design of indices are associated with different sources of profit on futures contracts. While the weighing and diversification methods place emphasis mainly on the behavior of current prices (spot), the maturity dates of the contracts are closely linked with the roll returns.

Hypotheses of Expected Rates of Return in the Commodity Markets Existing literature on commodity markets has proposed many theories and hypotheses to explain the existence of a risk premium for holders of long positions in commodity futures.6 Due to the fact that these premiums are strongly related to the shape of the forward curves (Walton 1991; Nash 2001; Till and Eagleeye 2003a,b, 2006; Chong and Miffre 2006; Kat and Oomen 2007a,b; Mezger 2008), most hypotheses are directly related to the theories of formation of the forward curves. Of course, there are exceptions such as the Capital Asset Pricing Model (CAPM), which, however, proved not to be satisfactory with relation to the discussed asset class. The following section of the study has been structured as follows: in the first place, the implications of the CAPM model for the expected returns on commodity futures markets will be presented, then a number of hypotheses that explain the evolution of forward curves, along with their consequences for the problem of risk premiums in the futures markets, will be described.

26

The Financialization of Commodity Markets

The prices of the underlying assets with future delivery may be higher or lower than the current prices. If the future price is higher than the current price (spot price), the situation is called the contango and we can say that the forward curve is increasing. If the futures price is below the spot price, it means that the forward curve is downward sloping, and this situation is called backwardation. In discussing the theory of forward curves, it is worth noting its similarity to the theory of yield curves in bond and interest rate markets (Spurgin and Donohue 2009, p. 123). In case of commodity markets, the nonspeculative players (also called the commercial players) are traditionally divided into producers and users. Similarly, in the bond market, the producers are bond issuers and consumers are their buyers or investors. In both cases, both the producers (issuers) and consumers (investors) have their preferred periods of maturity dates (redemption of bonds), from which, however, they may depart, if the market conditions lead them to do so. Moreover, in both markets, hedging positions (hedge) are open. In the bond market, the issuers and the investors may enter into hedging transactions against any changes in the interest rates, and in the commodity market, the producers and consumers may seek to neutralize the adverse price movements. The literature on the two markets uses different terminologies, but the similar characteristics make these terminologies quite close.

Systematization of the Hypotheses of the Expected Rates of Return In existing literature, four major groups of hypotheses explaining the expected rate of return on the futures markets can be found. The first group covers the CAPM model and is an extension of the Efficient Market Hypothesis (CAPM perspective). The second group is a hypothesis relating to the costs of storing and transporting the commodities (the theory of storage). The third group, the largest one, is focused on the specific expectations and motivations of individual market participants as well as on their impact on the supply and demand in the market (rational expectations theory, normal contango and backwardation theories, hedging pressure hypothesis, liquidity preference hypothesis, segmented market hypothesis, etc.). The fourth group includes alternative concepts, such as option models. For this study, however, a classification based on another line of theory is more important. Hypotheses explaining the shape of the forward curve are fundamentally different in terms of giving reasons for the existence of the risk premium in the markets. We can distinguish three broad categories of hypotheses. The first group includes the hypotheses according to which, in the futures contract market, there is no systematic risk premium for the holders of long or short positions. The second group is the opposite of the first one, because it justifies the presence of a positive risk premium for the holders of long or short positions. The third group

Passive Investment Strategies in Commodity Markets

27

admits the conditional existence of a premium at certain times and in certain market segments.

The CAPM Perspective According to the CAPM proposed by Sharpe (1964) and Lintner (1965), the determinant of the expected returns on the given portfolio in the market is the market beta coefficient in relation to the portfolio. In the context of the CAPM model, Lummer and Siegel (1993) as well as Kaplan and Lummer (1998) have concluded that the long-term rates of return (total return) on the GSCI index fully hedged with bonds should not be higher than bond yields. In other words, the excess return should be zero. This is a consequence of low correlations between the commodity markets and conventional asset classes such as stocks and bonds. It is consistent with earlier studies by Dusak (1973), who has noted very low beta coefficients relative to the stock market for futures quotations of wheat, corn, and soy. This does not mean, however, that there is no room for a positive risk premium in the commodity futures markets, but it does show that such a premium does not derive from its relation to the stock market alone. In addition, Erb and Harvey (2006a) point out that there is a wide series of issues that undermine the usefulness of the CAPM model in explaining the returns on commodity futures. Roll (1977) noted that the CAPM model suggests that there should be a linear relationship between the rate of return on the asset and the rate of return on the “market portfolio.” It should be noted that the market portfolio comprises not only stocks and bonds, but also real estates, works of art, human capital, or even utility objects such as cars or refrigerators. Roll challenged the notion that measuring the relationship between the asset and the stock market is the same as measuring the relationship with nonobservable and nonmeasurable market portfolios. What’s more, Black (1976) noted that commodity futures are not capital assets, but they are more like sports betting. Therefore, if the commodities are not capital assets and if they do not fit into the “market portfolio” basket, it is difficult to demonstrate why the CAPM model would actually explain their rate of return. Finally, it is worth quoting the analysis by Fama and French (1992), who point out that in the past, the CAPM model was not always an effective tool for predicting profits in the stock market; so if this model cannot cope with even the stock market, it is hard to understand why it would be effective in the case of commodities.

Theories of Storage Theories of storage are focused mainly on the motivations of the market participants who use commodities in the production process; to whom commodity futures markets are a place for purchasing a commodity or securing

28

The Financialization of Commodity Markets

its deliveries. This distinguishes these concepts from many later theories, which focus on speculators. Because of the negative connotations connected with this idea, it partially justified why, for many years, these theories have been treated harshly by the literature on investment in the commodity markets. Kaldor (1939), who is considered the founder of the theory of storage, noted that holding a reserve of raw materials has two implications: the cost of financing and storing raw materials and the benefits of holding the reserve, if commercially required. The first of these elements later came to be called the “cost of carry” and the other was referred to as the “convenience yield.” In his study, Kaldor (1939) defined the following equation. Future − spot = storage costs + interest costs − convenience benefit

The above equation, in the context of continuous capitalization, was then included into popular handbooks by, for example, Geman (2005) and Hull (2011). The theory of storage costs was later repeatedly interpreted (Working 1948, 1949; Brennan 1958; Telser 1958, 1960; Helmuth 1981; Brennan 1991; Erb and Harvey 2006a; Till 2008); it has also become an integral part of many later theories. According to the hypothesis of storage costs, the shape of the forward curve can be largely explained by levels of current and future inventory as well as by difficulty, and cost is associated with storage of the goods. Generally speaking, the theory of storage costs is based on the assumption that having an appropriate level of inventory is desirable to the company, because it can then avoid any downtime in the production process (Erb and Harvey 2006a). The less established the inventory, the greater is the risk of downtime, and the larger the inventory, the lower is the risk, of downtime, and thus, profitability. As a result, entrepreneurs may have a tendency to generate an additional demand in the spot market in order to meet their need to refill the stock, which gives them a real advantage. This advantage is referred to in the literature as the convenience yield (Kaldor 1939; Brennan 1991). The convenience yield is dependent on the inventory existing at the time. When the reserve is low, then there is a relatively high demand in the spot market and the convenience yield is high. As a result, the spot prices are relatively high in relation to the prices in the market of futures contracts with an offset maturity. When the reserve is high, the demand in the spot market is limited and so the spot prices are relatively low in relation to future prices. In other words, the level of stocks is positively correlated with the slope of the forward curve. Large inventories are usually associated with backwardation in the market, while small inventories are associated with contango. The interpretation by Erb and Harvey (2006a) assumes that the convenience yield can be seen as the risk premium associated with the level of inventory, which helps to explain the shape of the forward curve. This concept is somewhat in opposition to the ideas of Keynes (1930), according to

Passive Investment Strategies in Commodity Markets

29

which the entrepreneurs’ fear is also responsible for the risk premium, however this fear is not related to a production downtime, but rather to future price changes. Nonetheless, it should be noted that the theories of storage costs explain the periodic occurrence of positive returns that exceed the rate of the risk-free instruments in the selected markets. Theories of storage costs also suggest that the shape of the forward curve ought to be linked to how difficult and expensive the storage is. The goods, the storage of which is associated with significant costs, should usually have high convenience yields, but if the goods can be easily stored, the convenience yields should be rather low. In extreme cases, the goods cannot practically be stored. An example might be the cattle market, which is generally characterized by a descending forward curve (Helmuth 1981). It should also be noted that in the event of significant costs and difficulties with storage, the arbitrage along the forward curve is more difficult. As a result, depending on the ratio of the inventory to the demand, the curve may take a very steeply rising or falling shape (Spurgin and Donohue 2009). This problem concerns, for example, the mismatch between the level of the inventories in the natural gas market and the demand in the autumn and winter seasons (Till 2008). An element of the theory of storage cost, which is worth mentioning, is also the stock-out risk. This term refers to a situation when the stocks fall to zero and the consumption depends only on the direct production and transport (Spurgin and Donohue 2009). The risk of a stock-out of individual goods is highest during the peak season, such as winter for natural gas and fuel oil or at the time before the harvest for the grain market. The portions of the forward curve corresponding to those periods are usually associated with increased activity among users who want to hedge their investments, which would certainly affect the shape of the curve. The theory of storage cost has a solid foundation in the empirical research. Walton (1991) has noted that the commodities that are characterized by falling forward curves generate higher returns. Walton has explained this with the fact that these markets are usually characterized by relatively small inventories, which makes them vulnerable to disturbances in supply and creates a price premium for physically holding the goods from the spot market. In turn, the analysis carried out by Gorton, Hayashi, and Rouwenhorst (2007) has proved that the main determinant of the risk premium in the market is the quantity of stocks of raw materials at a given moment. These authors used the quotations of 31 commodities from 1969 to 2006 and noted that future rates of return could largely be explained by the variables correlated with the size of inventories, such as the base level.

Rational Expectations Hypothesis The rational expectations hypothesis implies that the futures price is equal to the current market expectations of future spot prices at the time of contract

30

The Financialization of Commodity Markets

execution. For example, if market participants expect an average increase in the price of the goods by 10 percent within three months, then the price of the futures contract with three-month maturity should be 10 percent higher than the spot price. This theory is based on the arbitrage mechanism existing in the market. The investors, who recognize that the future price is different from the expected future spot price, can make a spread (arbitrage) transaction involving the simultaneous purchase and sale of the underlying instrument and its derivative, and this way they ensure a profit. Hicks (1939) is usually considered the author of the rational expectations theory, but Evans and Honkapohja (2001) mention that the term “rational expectations” was also used by Hurwicz in his article from 1946. The rational expectations theory is valid in laboratory conditions (no transaction costs, taxes, restrictions on borrowing, investors’ neutrality toward the risk), but in practice, it has proven to not be useful in explaining the forward curves (Spurgin and Donohue 2009, p. 124). According to the rational expectations theory, when the market is in equilibrium, there is no room for the existence of a positive risk premium for the holders of long or short positions.

Normal Backwardation The normal backwardation theory, which was discussed, among others, by Hicks (1946), Bodie and Rosansky (1980), Fama and French (1987), as well as Gorton and Rouwenhorst (2006), was first proposed by Keynes in 1930. According to the normal backwardation theory, the producers of commodities have a large incentive to hedge their price risks in advance with short positions in futures or forward contracts. This is because, as they know the production costs in advance, they can secure a specified profitability. On the other hand, consumers prefer to buy in the spot market because it gives them more flexibility. The consequence is a systematic oversupply on a distant part of the forward curve, which is balanced by the speculators taking long positions with a discount on the spot price. Consumers, who have been tempted by an attractive discount, may also decide to take long positions on a distant part of the curve. According to the normal backwardation theory, in the market, a typical situation is a downward sloping yield curve. According to Keynes’ theory, the current futures price is lower than the expected spot price in the future. In such a situation, the quotations of the futures contract should gradually increase and, ultimately, on the maturity date, be equal to the spot prices. The consequence is a positive rate of return to holders of long positions. According to the normal backwardation theory, an additional risk premium for holders of long positions should be seen as the cost of hedging against a decline in commodity prices, which the producers and consumers pay to the speculators in a market.

Passive Investment Strategies in Commodity Markets St = 30

31

Current spot price

Market participants expect spot prices to decline (3$)

Spot and futures prices converge at expiration Expected spot price at expiration ST = FT = 27

Investor expects to earn the risk premium (2$) Ft = 25

Futures price at inception

Inception (t)

Expiration (t)

Figure 2.9 Risk premium according to the normal backwardation theory. Source: Weiser (2003), Gorton and Rouwenhorst (2006).

A good illustration of the mechanism behind the normal backwardation theory is an example cited by Gorton and Rouwenhorst (2006) following Weiser (2003) (figure 2.9). Let us assume that the price of oil is now USD 30 per barrel and the investors expect its further decrease to USD 27. However, the futures price in the market is USD 25 in order to “encourage” the speculative investors to trade, which means that the futures are sold with a fivedollar discount to the spot price. The difference between the expected spot price and the futures price ($ 2) is a risk premium that is offered to the speculators. Let us suppose that on the contract maturity date, the investor’s expectations have come true and the spot price has reached the expected level of $ 27. For an investor who is interested in changes in spot prices, this means a loss of $3 (30–27). However, a player in the futures market, who had opened a long position at the rate of $ 25, made a $2 profit. The above example assumes that the market expectations come true. Suppose, however, that the spot price on the maturity date is $26, which is below the forecasts of market participants. Then the futures market participant, who has taken the position at $25, is going to earn a dollar on every barrel. The achieved profit can be broken into two parts. The first component is the risk premium related to the difference between the expected spot price ($ 27) and the futures quotation at the time of the transaction ($ 25). In this case, the risk premium is $2. The second component is the difference between the actual spot price on the date of maturity and the original expectations (26 − 27) = −1.

32

The Financialization of Commodity Markets

Normal backwardation should also influence differences in rates of return on different futures contracts. Markets that are characterized by higher backwardation (higher positive difference between the expected spot and forward rates) should present a higher premium for the investors taking long positions. This thesis is, however, difficult to prove because the expected spot rate is inherently impossible to track. However, the existence of the long-term positive returns due to maintaining long positions in individual futures contracts should be a strong argument in favor of the theory proposed by Keynes (Erb and Harvey 2006a). The normal backwardation theory is analogical to the Preferred Habitat Hypothesis in the bonds market. The bond issuers generally prefer longterm debts, and the investors prefer short-term investments. As a result, the issuers have to offer higher yields on instruments with long maturity, which leads to an increasing yield curve. Unfortunately, the normal backwardation hypothesis has not yet found strong support in empirical research on commodity markets (Bodie 1983; Kolb 1992; Gorton and Rouwenhorst 2006; Scherer and He 2008).

Hedging Pressure Hypothesis The hedging pressure hypothesis is an important extension to the normal backwardation theory. Cootner (1960), Deaves and Krinsky (1995) and Erb and Harvey (2006a) suggest that the normal backwardation theory assumes that the hedging players always have a long position in the underlying assets, thus they hedge themselves by taking a short position in futures contracts. As a result, the forward curve is downward sloping and offers the possibility of earning a profit for the speculators taking long positions. These authors suggest that the risk premium can be offered in the markets both in backwardation and in contango, depending on whether hedging players take long or short positions. Anson (2000) makes a distinction between the markets where the hedging producers dominated and those in which the hedging users prevail. For example, crude oil producers, who, because of the nature of the business they carry out, have a long position in the oil market, try to reduce their exposure to risk, by taking short positions in the futures market. The transactions made by the oil manufacturer make the forward curve decrease and thus creates a positive risk premium for the investors taking long positions. On the other hand, in the aluminum market, for example, consumers who use aluminum in their production processes may have a dominant position. In order to reduce fluctuations in the level of costs, they take long positions in the futures contracts, giving rise to a raising forward curve. Then the risk premium will be assigned to the speculators taking short positions. In summary, according to the hedging pressure hypothesis, both portfolios, consisting of both long and short positions, may generate a risk premium.

Passive Investment Strategies in Commodity Markets

33

Both hypotheses are related to forward curves; the normal backwardation theory and the hedging pressure hypothesis provide room for a positive risk premium in the market. It is the hedge cost that must be borne by the speculators for the benefit of the hedging entities. Hedging pressure theory, however, is a more flexible extension of the normal backwardation theory, because it justifies the possible presence of a risk premium regardless of the dominant position of the hedging entities. Existing literature provides a good deal of evidence supporting the hedging pressure theory. Carter, Rausser, and Schmitz (1983) have found that the risk premium occurs periodically, and is variable in time. Bessembinder (1992), upon examining 16 futures between 1967 and 1989, has noted that there is a relationship between the average rates of return and net hedging positions. Similar conclusions have been drawn by de Roon, Nijman, and Veld (2000), who have noted that hedging pressure serves to explain the rates of return generated by 20 futures contracts that they surveyed, which were quoted between 1986 and 1994. A slightly later article by Till and Eagleeye (2003a) cites Nash and Smyk (2003) in holding that the term “structure” is an effective predictor of future rates of return, and this view has later been confirmed by Chong and Miffre (2006) as well as by Mezger (2008). The aforementioned Till and Eagleeye (2006) in another text have tested and confirmed the effectiveness of a strategy based on taking positions in commodities characterized by a clearly negative base. Evidence in support of the hedging pressure hypothesis has also been discovered by Maddal and Yoo (1991), de Roon, van den Goorbergh and Nijman (2005) as well as by Dincerler, Khokher, and Simin (2005). Interestingly, the literature also provides arguments that suggest that the hedging pressure may also be a source of profit; for example, in the joint-stock futures markets (Zaremba 2011a).

Liquidity Preference Hypothesis The liquidity preference hypothesis is referred to by Spurgin and Donohue (2009) as an analogy explaining the shape of the yield curve in the bond market. This hypothesis refers to a situation, in which entrepreneurs have no incentive to hedge the prices of their deliveries in the futures market, since they can easily pass them on to the final users. The gasoline market might be a good example here. According to Spurgin and Donohue (2009), the liquidity preference hypothesis is an extreme case of the normal backwardation theory, where none of the hedging entities has long positions, so all the long positions in the market are held by investors (speculators).

Segmented Market Hypothesis The segmented market hypothesis proposes that, due to various determinants, a market of a single product is divided into “segments,” which

34

The Financialization of Commodity Markets

operate independently of each other (Spurgin and Donohue 2009). The most common example is geographical segmentation, as in case of gas prices in Europe and North America. Nonetheless, the market can be divided along the time axis when completely different actors operate in the spot market and in the futures market. In such a case, the shape of the yield curve will have no larger load of information because the market participants are not interested in other segments. The segmented market hypothesis conceives the existence of higher positive returns for the holders of long positions than do the rates of risk-free instruments for selected periods and in selected markets.

Normal Contango Normal contango is a concept introduced by Keynes, followed by Spurgin and Donohue (2009) to describe markets where the buyers of goods have the greatest incentive to hedge their investments. This is the characteristics of the natural gas market, for example, in which public utility companies usually buy more gas in the futures markets than they actually require, in order to hedge their investments against any unexpected increases in demand. A result is a growing forward curve. Normal contango hypothesis assumes the existence of higher positive returns for holders of short (not long) positions than the rates of risk-free instruments in selected markets could hope to provide.

Option Models Spurgin and Donohue (2009) distinguish two types of option models that explain the shape of the commodity forward curve. The first model is associated with production costs (Litzenberg and Rabinowitz 1995). While prices in the spot market may temporarily fall below the cost of production, the manufacturer will not conclude a futures contract agreeing to sell a commodity at a price cheaper than the price of manufacture. Instead of accepting a loss, the manufacturer can always stop production. This option, embedded in the price, limits the descending slope of the forward curve. The second model links the shape of the curve with the level of inventories (Milonas and Thomadakis 1997; Zulauf, Zhou, and Robert 2006). Due to the fact that inventory shortages are usually more acute than inventory excesses, the commodity markets are usually characterized by higher volatility when the prices are rising than when they are falling. The asymmetry of the volatility makes it safer to hold physical (spot) resources or short-term contracts than long positions in long-term contracts. This situation results in a descending forward curve.

Passive Investment Strategies in Commodity Markets

35

The option approach is also discussed by Markert and Zimmermann (2008). * *

*

As can be deduced from the aforementioned theories concerning the structures of commodity markets, the shape of the forward curve is crucial when it concerns the long-term profits expected by the speculators. It must be noted that in various theories (hedging pressure, normal backwardation, storage), the premium resulting from the rollover is explained differently, but it always is a primary source of profit for the investors maintaining long positions in the market. That view is consistent with the views of many authors exploring the subject (Cochrane 1999; Nash 2001; Nash and Shrayer 2005; Till 2007c following Feldman and Till 2006; Till 2007d). As a result, it is very important that if the phenomenon of financialization of the commodity markets has contributed to changes in the term structure and roll returns, it could also have a very significant impact on the validity of investing in the commodity markets. This issue will be discussed in more detail further into this book.

Benchmarks for the Commodity Markets The choice of the benchmark7 is crucial to an analysis of the suitability of a class for constructing an investment portfolio. In accordance with the hints by Sharpe (1992), the benchmark should be a cost-effective investment alternative that has been identified in advance. For traditional asset classes, there are many recognizable indices in the market, which are calculated by many institutions, and they can be adopted as a benchmark for an appropriate investment. The design and selection of the appropriate benchmark for a commodity market, however, is a more complex task. An interesting approach to the subject of benchmarks for the commodity markets can be found in the article by Erb and Harvey (2006a). The authors suggest, for example, that the idea of developing a capitalization-weighted index, which is quite common for the stock and bond indices, cannot be mapped to commodity futures because the numbers of open long and short positions are equal, so the total “net” market value (long and short positions) is zero. As a result, there is no single, generally accepted method of weighing the instruments in the index, so the indices existing in the market are fundamentally different in terms of composition of the portfolio and rebalancing principles, and consequently, also in terms of characteristics of returns.

Design of the Indices in the Commodity Markets Market practice has formed three types of commodity indices that correspond to three main sources of profit associated with futures contracts: spot

36

The Financialization of Commodity Markets

yield, roll yield, and collateral yield. Each index has its own information value.8 Spot Indices Spot indices represent changes in the market value of the contracts included in the index basket before taking into account the impact of changes in the composition of the portfolio. This means that the spot indices reflect only the spot yield associated with the change in market prices, not the investors’ profit. The spot indices can, by analogy, be compared to price indices in the stock market. Excess Return Indices Excess return indices represent changes in the market value of the contracts included in the index basket, after taking into account changes in the composition of the portfolio. This means that the rate of return on the excess return index takes into account two components: the spot return and the roll return. After subtracting the return on the spot index from the spot return on the excess return index, the roll yield is obtained. We can assume in a simplified way that the excess return indices reflect a risk premium generated in the commodity market over a given period. Total Return Indices Total return indices are analogous to the income indices in the stock market because they represent the revenues generated in the commodity markets in a most complete manner. The total return index shows the rates of return from the same strategy, as in case of the excess return, however, assuming that the position is fully colletarized. In practice, it is usually assumed that the US Treasury bills are collateral, so total return is a yield on the excess return plus return on T-Bills (that is, a collateral return). In other words, after subtracting the return on excess return index from the total return index, we can calculate the collateral yield.

Overview of the Commodity Market Indices Currently, several institutions that calculate various kinds of commodity indices exist in the market. The products differ substantially from each other, even in terms of the adopted methodologies or length of their track records. Among the most important differences in the design of each index are the selection of commodities, weighting method, portfolio rebalancing scheme, and return calculation method (Raab 2007). The oldest indices include the Reuters/Jefferies-CRB Index and Standard&Poors Goldman Sachs Commodity Index (S&P GSCI), which was introduced in 1991. It is a common practice in case of commodity indices to calculate and present their hypothetical values based on the existing methodology before the date the index is introduced (the so-called backfilling). On

Passive Investment Strategies in Commodity Markets

37

this basis, GSCI has been calculated back to 1970, and newer indices, such as the Rogers International Commodity Index and Diapason Commodity Index, are calculated back to 1984 and to 1996, respectively. Another important difference is the number of indices calculated by the providers. Companies usually publish not only a composite index for the whole commodity class, but also an index for each subclass, such as energy raw materials, agricultural raw materials, industrial metals, ores, etc., but, in addition, they publish return indices: spot, total, and excess indices, which are usually calculated for each group. Some providers also offer subindices with alternative weighing methods. For example, Deutsche Bank, besides its “classical indices,” currently publishes the optimum yield indices, which outweigh the contracts characterized by the normal backwardation and the mean reversion indices, thus increasing the involvement in commodities that have historically recorded a lower rate of return than the broad asset classes. As a result, the number of indices may range from only a few in case of Bache, up to almost 100 as calculated by Dow Jones–UBS. Typically, most providers calculate from 20 to 30 indices. A number of created subindices are closely related to the criteria for the selection of commodities to the index. The most common criteria include market liquidity and importance to the global economy, understood as the share in global production or consumption. In addition, some indices also take into account less liquid and less important groups of commodities in order to achieve the required diversification; this is seen, for example, in the Rogers International Commodity Index. As a result, even the number of commodities under consideration is different. The widest universe forms a basis of the Diapason Commodity Index (50 markets) and Rogers International Commodity Index (38 markets), whereas, for example, the Deutsche Bank Liquid Commodity Index represents only six commodities, which are characterized by very high liquidity (WTI crude oil, heating oil, aluminum, gold, wheat, and corn). The next step in designing the index is, upon selecting the commodities, choosing the weighing method. Also, in this respect, there are clear differences. Some index providers rely on some form of measuring the global production; DJ-UBS, for example, takes into account production from the previous year and S&P GSCI, the average production of the preceding five years. Other common variables are the trading volume and the number of open positions. Equal-weighted indices and the indices employing historical price behaviors exist in the market. It is also worth mentioning that some providers impose certain a priori limits in the context of diversification, in order to minimize the chance that single market quotations dominate the entire index. An example is the JP Morgan Commodity Curve Index, which has a built-in maximum exposure limit for energy raw materials: 33 percent. Table 2.6 shows the weights of the most popular indices in the year 2014. It is apparent that the indices are significantly different, which affects the quotations.

The Financialization of Commodity Markets

38 Table 2.6

Weights in the selected commodity indices in year 2014 CRB (%)

DJ-UBS (%)

RICI (%)

GSCI (%)

Energy

17.6

31.8

40.0

68.8

Grains

17.6

22.9

20.0

12.0

Industrial metals

11.8

16.6

14.0

6.7

Meats

11.8

5.1

3.0

5.0

Soft commodities

23.5

7.9

11.9

3.3

Precious metals

17.6

15.7

11.1

3.2

Source: www.mrci.com/client/crb.php; http://www.spindices.com/documents/index-news-and-announce ments/20131107-sp-gsci-composition-and-weights-2014.pdf; http://www.rogersrawmaterials.com/weight .asp; http://press.djindexes.com/index.php/2014-weights-for-the-dow-jones-ubs-commodity-index-announced -by-sp-dow-jones-indices-and-ubs-investment-bank/

Most indices are rebalancing indices, that is, they adjust the weights of each commodity in the index, with a similar frequency. The index is usually rebalanced once a year. The exceptions are the Diapason Commodity Index, which is rebalanced once a month, and the RJ/CRB, which, due to its weighing method, is rebalanced continuously. Futures contracts that are used in the indices may come from multiple markets located in different countries. Most indices are based on prices in the United States and Great Britain, although S&P GSCI, for example, is based only on the prices in the US markets. The quotations from the exchanges located in other countries (Canada, Japan, or Australia) are quite rare, but they are considered, for example, in the Rogers International Commodity Index. The adopted technique of rolling contracts has had a major impact on the final rate of return and on the volatility of the index. The most common solution is to acquire contracts with the shortest maturity and then roll them—on the last day (or in the last few days) of trading—over to the next series. Nonetheless, there are exceptions to this rule. The CX Commodity Index always rolls the positions to the contract that is most liquid. This means that the most liquid contracts exert the strongest influence on the performance of the index. In turn, the CRB uses a method called forward averaging, in which it is rolled to the arithmetic mean of two to five contracts expiring within six months. Two relatively new indices, the JP Morgan Commodity Curve Index and the UBS Bloomberg Constant Maturity Commodity Index, use completely different methods. The first-mentioned index takes into account not just a single selected contract but also the complete forward curve of all series of the futures. The rollover occurs every tenth day of the month and covers all series of contracts. The concept of the UBS index is also innovative because, in accordance with the adopted methodology, the provider of the UBS Bloomberg calculates multiple indices, each of which has its own execution time. Each index consists of two futures and its date is nearest to the

Passive Investment Strategies in Commodity Markets

39

date of their maturity. These instruments are weighted in such proportion that the maturity date for the entire index is maintained. Rollover occurs continuously, so that the a priori set out maturity date is not changed. When discussing the differences between each index, one noteworthy fact should be mentioned: almost all the indices assume investing only in long positions within each commodity. In this context, the exception is the Mount Lucas Management (MLM) Index, which, depending on the momentum in each market, assumes long positions while it is also designed to have an exposure to currency pairs. As a result, it is a more appropriate benchmark for managed futures asset classes than for investments in commodities. Nonetheless, it is customarily classified as a commodity market index. Table 2.7 presents summarized information on the commodity markets that was published at the time of writing this book. Due to the diversity of design, the commodity indices differ in average rates of return and standard deviations. Figures 2.10 and 2.11 show the behavior of total return indices recorded in the period from 1998 to 2014. It is apparent that commodity indices are characterized by a great diversity in terms of the generated results. Even if we skip the MLM Index, the average annual rate of return ranges from 4 to 9.2 percent, and the risk is measured with standard deviation on rates of return ranging from 12.2 to 20.8 percent. For this reason, selection of an appropriate index that actually displays the characteristics of an asset class that is a passive investment in the commodity market is very important. * *

*

The commodity market is fragmented and diverse. From the investor’s point of view, several separate, often uncorrelated sources of profit exist in the market, which are reflected in the design of the commodity market index. That is why it is very important to choose an index that fairly reflects the investments in the commodity market. For this purpose, such an index should meet several criteria. First, the index should be calculated in the form of total returns, so as to reflect the income of the investor. Second, it should have a long enough track record in order to be able to analyze structural market changes, which means at least 15 years. Third, the index should have a passive strategy of reconstruction, so that the profits are not a result of active strategies based on trend following, for example. This aspect will be considered in the section devoted to managed futures as an example of an active investment. Fourth, the index weighting method should refer to the actual exposure of the investors in the commodity market (e.g., through the trading volume or the number of open positions), and not to the significance to the global economy. Such a structure better reflects the investor’s point of view. Fifth, the index should be well diversified not only in terms of individual commodities and their groups, but also in terms of contract maturity. Excessive focus on a selected market sector (e.g., energy raw materials in case of S&P

Table 2.7 Commodity market indices Symbol

CRB/Reuters

DJ-UBS

RICI

S&P GSCI

DBLCI

Name

Reuters/ Jefferies-CRB Index

Dow Jones-UBS Index

Rogers International Commodity Index

Standard& Poors Goldman Sachs Commodity Index

Deutsche Bank Liquid Commodity Index

Index types

SR, TR, ER

SR, TR, ER

TR, ER

SR, TR, ER

TR, ER

Base dase

January 1967

January 1999

January 1984

January 1979

December 1988

Starting date

January 1957

January 1991

January 1984

January 1979

December 1988

Investable since

January 1986

January 1998

January 1998

January 1994

March 2003

Number of subindices

19

22

38

24

6

Criteria of commodity selection

Diversification

Liquidity and diversification

Global demand

Global production and liquidity

Six commodities representative for distinct sectors

Selection of futures contracts

2–6 contracts with maturity up to 6 months

Nearby contract

Nearby contract

Nearby contract

Future contract with the highest roll return

Futures source

International markets

International markets

International US markets markets

Diversification limits

Many limits

33% per sector, 2–15% per commodity

No specific limits

No specific limits

No specific limits

Weighting method

Equal weighted

Average liquidity and production

Wolumen produkcji

Average world production

Wolumen obrotu

Index calculation

Geometric average

Arithmetic average

Arithmetic average

Geometric and arithmetic average

Arithmetic average

Release frequency

Daily

Daily

Daily

Daily

Daily

Rolling frequency

Monthly

–

Monthly

Monthly

Monthly

Rebalancing frequency

Constant

Yearly

Yearly

Yearly

Yearly

International markets

Source: Author’s compilation based on following sources: https://customers.reuters.com; http://www.bloomber gindexes.com; https://www.djindexes.com; http://www.rogersrawmaterials.com/home.asp; https://us.spindices .com; https://index.db.com; www.dax-indices.com; https://www.mtlucas.com; http://www-pa.prudentialbache. com; http://www.ml.com/media/67354.pdf; Diapason Commodities Index® Manual 2013; https://www .jpmorgan.com/directdoc/CCI_Introduction.pdf; Wikipedia, Fuss, Hoppe, and Kaiser (2008, pp. 173–174). The table was compiled in the beginning of 2014; however, some data may come from older sources.

CXCI

MLM

CX Commodity Index

BCI

MLCX

DCI

JPMCCI

CMCI

Bache Mound Commodity Lucas Management Index Index

Merrill Lynch Commodity Index eXtra

Diapason Commodities Index

JP Morgan Commodity Curve Index

UBS Bloomberg Constant Maturity Commodity Index

SR, TR, ER

TR

TR

TR, ER, SR

TR, ER, SR

TR, ER, SR

TR

November 2006

January 1988

January 2007

June 2006

June 2006

November 2007

February 2007

December 1996

January 2000

January 1990

–

December 1996

December 1991

October 1997

November 2006

January 1988

January 2007

June 2006

Nie inwestowalny

November 2007

February 2007

21

22

19

18

50

33

27

Open interest

Volume and open interest

Liquidity Liquidity and importance for global economy

Importance for global economy

Diversification

Diversification

Contract with the highest number of open interest

Trendfollowing

–

Nearby contract

Full commodity curve

Two futures with a defined maturity

Nearby contract

International International United markets markets States and United Kingdom

International International markets markets

International markets

International markets

No specific limits

No specific limits

Many limits

3–60% per sector

Some limits

Up to 33% in energy commodities

–

Open interest

Equal weighted

Trend following

Importance for global economy

Liquidity and importance for global trade

Arithmetic average

Arithmetic average

Arithmetic average

Arithmetic average

Arithmetic average

Arithmetic average

Arithmetic average

Daily

Daily

Daily

Daily

Daily

Daily

Daily

Dynamic

Monthly

Daily

–

Monthly

Monthly

Constant

Yearly

Monthly

–

–

Monthly

Yearly

Yearly

Production, consumption, & liquidity

The Financialization of Commodity Markets

42 1,000 900 800

CRYTR

DJUBSTR

RICIGLTR

SPGSCITR

DBLCMAVL

MLMCCOD

BCIPT

MLCITR

DCI TRUS

JMCXTR

CMCITR

700 600 500 400 300 200 100 0 1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Figure 2.10 Performance of the commodity market indices during the period from 1998–2014 (rebased indices August 1998 = 100). Source: Author’s calculations.

10% CMCITR

9%

DBLCMAVL

Annual rate of return

8%

BCIPT

7% 6%

DCI TRUS JMCXTR CRYTR

5%

RICIGLTR MLCITR

DJUBSTR

4%

SPGSCIR

3% 2% 1% 0% 10%

MLMCCOD 12%

14%

16%

18%

20%

22%

Annual standard deviation

Figure 2.11 Average annual rate of returns and standard deviations in the commodity market 1998–2014. Source: Author’s elaboration.

GSCI) or spot rollover of the futures contracts in the curve may distort the real picture of the exposure of the investors to the market. The only index that currently meets all these criteria is the JP Morgan Commodity Curve Index, so it will be used for further research on the validity of holding commodities in a portfolio.

Passive Investment Strategies in Commodity Markets

43

Performance of Passive Investment Strategies in the Commodity Markets in the Light of Previous Studies The literature points to a number of advantages to investing in commodities that include hedging against inflation (Bodie 1983; Froot 1995; Till and Eagleeye 2003b; Gorton and Rouwenhorst 2006; Akey 2007). For this text, however, two other properties are primarily important. The first one is the existence of a positive risk premium in the commodity markets, which would justify their use as stand-alone investments. The second one is the ability to diversify a portfolio composed of traditional asset classes (stocks and bonds)in a manner that allows the investor to move the curve effectively, and thus justify holding commodities in the portfolio. We later present an overview of current research on both aspects of passive investments in the commodity markets. The review has been carried out in a chronological order and closes with a synthesis and key conclusions. Although futures contracts on commodities are already more than a century old, a detailed body of research on the possibilities of generating long-term rates of return started as late as in the 1970s (Till 2007a). First, research focused primarily on the agricultural market in the United States and did not yield promising results. Dusak, who, in 1973, analyzed the quotations of individual products in the years 1952–1967, was not able to confirm the existence of a positive risk premium. A breakthrough in this field was a study by Greer (1978), which discussed commodities as an investment asset class. Greer showed that the risk of investing in the futures contracts based on commodities could be significantly reduced with full collateral. With his own price index from years 1960 to 1974, he calculated that the investment in commodities had brought higher returns than the investment in stocks and, at the same time, allowed for a lower maximum subsidence of capital. Greer also carried out probably the first scientific analysis of using commodities in the structure of the investment portfolio. He demonstrated that a rebalanced portfolio composed of stocks and bonds would bring more stable and higher returns than a stocks-only portfolio. In 1980, Bodie and Rosansky also confirmed the existence of a positive risk premium in the commodity market, and their study was one of the most-cited books on the subject. The authors analyzed quotations of 23 commodity futures in the years 1950–1976 and in 22 cases, they were able to confirm the existence of a positive risk premium; nevertheless, the statistical significance of the results was low. The reason was considerable volatility in individual contracts; thus Bodie and Rosansky decided to also carry out calculations for an equal-weighted commodity index, which provided statistically significant rates of return. These results were confirmed four years later by Bodie (1983) as well as by Carter, Rausser, and Schmitz (1983), who also noted that the amount of the risk premium was subject to significant fluctuations over time. Shortly thereafter, Chang (1985) confirmed the existence of a

44

The Financialization of Commodity Markets

changing time risk premium during the years 1951–1980 in the markets of wheat, corn, and soy. Fama and French (1987), who analyzed the behavior of the prices of 21 commodities from the years 1966–1984 also focused on agricultural markets. A positive risk premium was confirmed only in seven cases. Bessembinder (1992) noted that the presence and amount of the risk premium depends on the term structure and Bjornson and Carter (1997) also observed that the risk premium was historically associated with macroeconomic variables: economic growth, inflation, and interest rates. Higher risk premiums in the past existed where the curve was downward sloping, while the lower risk premiums were generally correlated with the increasing curve. Similar conclusions were drawn by Chong and Milfre in 2006. Whereas Kaplan and Lummer (1998) focused not so much on individual markets, but rather on the investment in a full commodity index and demonstrated that in the years 1970–1997, the investments in the GSCI index hedged with bonds generated higher rates of return than the stocks, but at a higher risk. Later, the rates of return in the commodity markets were analyzed, inter alia, by Greer (2000); Till (2000a,b); and Dunsby, Eckstei, Gaspar, and Mulholland (2008). The most recent studies on the existence of a risk premium in the commodity markets focus largely on differences between the risk premiums for indices and for individual commodities. Garcia and Leuthold (2004) confirmed the existence of a risk premium for the commodity index from the years 1982 to 2004, but did not find conclusive evidence of the same being applicable to individual instruments. Anson (2006) calculated that between 1970 and 2000, the diversified commodity portfolios recorded higher returns than the stocks and bonds, but at a slightly higher risk, and Erb and Harvey (2006a) concluded that the ability to know whether or not the risk premium exists in the market depends firmly on the adopted research methodology. In the same year, an article by Gorton and Rouwenhorst titled “Facts and Fantasies abort Commodity Futures” was published, which to this day remains one of the most widely read scientific articles on the commodity market. The year 2004, when the research by these authors saw the light of day in the form of a working paper, is considered by many authors as the symbolic birth of the commodities-as-an-investment asset class (Rogers 2007; Authers 2010). Gorton and Rouwenhorst confirmed, with high statistical significance, the existence of a risk premium for the index of 36 commodities in the years 1959–2004. In the authors’ opinion, a diversified commodity portfolio in this period generated rates of return similar to the stock market, but at a lower risk. Nevertheless, the researchers did not obtain conclusive evidence of the long-term positive returns for individual markets. Most of the later studies were actually a confirmation of the results obtained by Gorton and Rouwenhorst. Kat and Oomen (2007a) did not obtain convincing evidence of the existence of a risk premium for the majority of the wide spectrum of 42 commodities that they analyzed in the years 1965–2005. Similar conclusions were drawn by Scherer and He (2008), who studied the quotations of individual contracts in the years

Passive Investment Strategies in Commodity Markets

45

1989–2006 and did not confirm the existence of a risk premium, although it turned out to be statistically significant for the commodity indices calculated for this period by Deutsche Bank. Woodard, in a study carried out in 2008, noted that although only three of the nine markets that he analyzed generated long-term positive returns, they were significantly positive for the relevant commodity indices. The long-term positive returns on the indices were also emphasized by Hafner and Heiden (2008) in their study concerning the period between 1991 and 2006, by Fuss, Hoppe, and Kaiser (2008) in their analysis of the quotations from 2001 to 2006 as well as by Shore (2008) in their observations of the GSCI index in the period from 1969 to 2006. Positive returns for the period 1970–2006, exceeding the performance of stocks and bonds, were also noted by Nijman and Swinkels (2008), although the authors point out that the volatility of this asset class in the examined period was higher than that of more conventional markets, that is, stocks and bonds. In parallel to the studies on the ability of the commodities to generate long-term positive returns and risk premiums, there was a discussion on the role of commodities to a broadly defined investment portfolio and their contribution to portfolio diversification. The first scientific analysis of the use of the commodities in the structure of the investment portfolio was probably carried out by Greer in his pioneering study of 1978. He demonstrated that a rebalanced portfolio, composed of stocks, bonds, and commodities, generated more stable, higher returns than a portfolio consisting purely of stocks and bonds. Just two years later, Bodie and Rosansky (1980) noted that the allocation of 40 percent of the portfolio to commodity futures significantly decreased the risk while increasing the returns. Similar conclusions were drawn by Jaffe (1989), who, in turn, focused exclusively on gold. According to Jaffe, investing a part of the portfolio in futures contracts on gold decreased the risk and increased the returns on a diversified portfolio in the years 1971–1987. Satyanarayan and Varangis (1994) showed that the use of a commodity index (GSCI) made it possible to create a portfolio with returns not lower than a portfolio of global stocks and bonds, but with less risk. Froot (1995) concluded that commodities diversify a portfolio better than real estate or shares in, for example, commodity companies. Allocation of some part of the portfolio in a commodity index extends the efficient frontier, but this happens only if the index is dominated by oil. The usefulness of commodities to the optimization of a portfolio (consisting of stocks and bonds) in terms of risk and return parameters was also confirmed by Kaplan and Lummer (1998) in relation to the GSCI index; by Fortenberry and Hauser (1990) in relation to the agricultural raw materials in the years 1976–1985; and by Jensen, Johnson, and Mercer (2000), also in relation to the GSCI, but for the period between 1994 and 2006. Woodard, Egelkraut, Garcia, and Pennings (2006) concluded that the allocation of 10 percent of the portfolio to stocks and bonds in a commodity index, between 1970 and 2000, resulted in moving the efficient curve upward, and these studies were later confirmed over a slightly extended period of research by Anson (2006).

46

The Financialization of Commodity Markets

Idzorek, in a study carried out in 2006, and ordered by Ibbotson Associates, demonstrated that commodities were poorly correlated with stocks and bonds as they were usually positively correlated with unexpected inflation, which could not be said of traditional asset classes. Kat and Oomen (2007a) showed, in turn, that investing a part of the portfolio both in the GSCI index and individual commodities in the years 1996–2006 allowed for an increase to the Sharpe ratio of the investment portfolio, and Woodard (2008) showed that the commodity indices (developed by Deutsche Bank) provided a positive risk premium in the years 1989–2006, which could not be explained by the rate of return on stocks and bonds. Scherer and He (2008) as well as Shore (2008) also emphasized the movement of the efficient frontier of the portfolio of stocks and bonds through investing in the commodity market. Interesting results were achieved by Nijman and Swinkels (2008), who focused on gold futures. According to the authors, allocation of a part of the portfolio to gold for a major part of the investment period does not improve the portfolio characteristics, and the few cases showing the beneficial effect of the said ore concerned only assets denominated in US dollars. Among the most recent studies on the role of commodities to an investment portfolio, we should mention the text by Heidorn and Demidova-Menzel (2008). The authors had analyzed the behavior of the portfolios consisting of stocks, treasury bills, corporate bonds, and real estate in the years 1973–1997 and came to the conclusion that, depending on risk aversion, the investors should invest from 5 to 36 percent of their portfolios in commodities. * *

*

The review of the literature leads us to several conclusions. First, the literature provides much stronger evidence of the existence of a risk premium with respect to full portfolios rather than in the case of individual commodities. This phenomenon is explained mainly by the specific nature of the structuring, weighing, and rebalancing of the commodity portfolios (Erb and Harvey 2006a). Second, the risk premium is not constant and can vary, and this volatility may be partially explained by, for example, the term structure of the market. Third, apart from the interesting properties of commodity portfolios as stand-alone investments, they are also useful to the design of an investment portfolio consisting of multiple asset classes, and this usefulness is manifested in the fact that the commodities allow the investor to build more efficient portfolios in terms of the risk-return relationship.

Chapter Three Active Investment Strategies in Commodity Markets This chapter describes managed futures as an example of an active investment strategy in the commodity markets. The aim of this chapter is to make a detailed presentation and provide characteristics of this investment asset class, particularly in terms of the operation and the selection of the proper benchmark for empirical research on financialization of the commodity markets. The chapter consists of several parts. At the beginning, we examine the brief history of futures funds. Next, the institutional regulations in the United States, where managed futures come from, are described. The subsequent section focuses on futures funds management strategies. Another element is the characteristics of indices of managed futures existing in the market, taking into account the potentially related data biases. The chapter ends with a review of the existing research on the advisability of investing in managed futures.

The Genesis of Managed Futures For a long time, speculative investments in futures contracts were primarily associated with independent traders who concluded transactions directly in the commodity exchanges. These investors were often able to generate impressive rates of return, which raised a lot of interest in the opportunity to achieve profits through speculating on futures contracts. Skills of the traders were recognized by other market participants, who concluded that their talents could be used simultaneously on many markets—including, for example, a currency market—which would allow the effective reduction of risk through diversification. Unfortunately, the structure of the futures market from the middle of twentieth century was not conducive to such initiatives. It is commonly assumed that the first investment fund operating in the market of commodity futures contracts was Futures Inc., established in 1948 by Richard Donchian. The fund operated successfully until 1960 and had many followers. Richard Donchian himself is now commonly regarded as the father of the managed futures industry as well as the originator of the

48

The Financialization of Commodity Markets

concepts of automated transactional systems and the strategy of trend following (Faith 2008). The development of investments in futures contracts also gave birth to intensive research and academic publications that focused not only on market microstructure, but also on the profitability of investing in futures contracts (Jaffarian 2009, p. 170). Initially, the managed futures industry developed slowly; the multiple investment firms that have contributed to it its current shape appeared as late as in the second half of the 1960s. An important step was the establishment of the first database in 1967 that gathered futures contracts quotations. As a result, it became possible to test automated transactional systems, which intensified development of the sector (Chandler 1994, p. 17). Shortly thereafter, the Commodities Corporation was founded, an institution that has proven to be extremely important for the development of the industry. The Commodities Corporation was a research institute that financed and supported analyses of the futures market, while at the same time assisting in implementing the transactional systems. It was the Commodities Corporation where the careers of many celebrities of the managed futures industry, such as Bruce Korner, began (Schwager 2006). Eventually, the company was acquired by, and became part of, the Goldman Sachs investment bank. The origins of the managed futures industry are traditionally associated with the United States, since its development in Europe began several years later. Conti-Commodities, which offered its customers management of their money through investments in trend following systems, may be considered the first such company in Europe (Chandler 1994, p. 18). The Conti Group was founded in Belgium in 1913, but only at the end of the twentieth century did it begin to provide the above-mentioned services. Another breakthrough in the development of the industry occurred in the 1980s, and it was primarily of an institutional nature. Then, in addition to traditional accounts managed by the Commodity Trading Advisors (CTAs), investment funds that offered managed futures investments to retail customers started to appear (Jaffarian 2009, p. 170). The result was a rapid development of the sector and the increase in assets under management (AUM), from two billion USD in 1983 to more than ten billion USD five years later. The growing popularity of investments in futures contracts had an impact on the academic world. In the 1980s and 1990s, research on these markets branched out and went in two directions. The first one was the analysis of futures investments in long positions and the justification for their appearance in the construction of the investment portfolio. Since then, a number of publications to justify the inclusion of long positions on commodity futures into the portfolio, of which the most famous is probably the article by Gorton and Rouwenhorst of 2006 (Gorton and Rouwenhorst 2006), have appeared. Although at present the commodities still represent a small part in most portfolios of the individual and institutional investors, recently, an increased interest in this asset class can be seen, and it manifests not only in numerous papers on this subject (e.g., Rogers 2004), but also in the

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49

emergence of a number of funds, which employ long-only passive investment strategies. The second stream of the research has focused on actively managed portfolios operating in the market of futures contracts, and their contribution to the diversification and overall performance of the portfolio. Nonetheless, in conclusion, the academic world remains divided. Arguments in favor of investing in managed futures include, among others, substantial historical performance, low correlation with traditional asset classes, and dynamic growth of assets in the industry, which is the best sign of support from customers. However, some researchers debate whether the managed futures can be regarded as an asset class, because the staff managing portfolios of futures contracts may take both long and short positions; so the rates of return they achieve are only a result of their skills and analysis and are not due to the assets they hold. What’s more, there are many information biases that could potentially distort and inflate the performance of the managed futures investment class, so it is difficult to assess its attractiveness. In 2009, assets worth two hundred billion USD were under the management of the managed futures industry, and in the United States, 902 entities were registered as CTAs and 397 entities as Commodity Pool Operators (CPOs) (Jaffarian 2009, p. 171). This state of affairs shows the managed futures industry to be a rather small area on the map of the global financial market, but the favorable investment results achieved during the collapse of the market in the years 2007– 2009 draw the increased attention of investors. According to statistics by BarclayHedge, the AUM of the managed futures funds at the end of the year 2013 amounted to more than 330 billion USD (figure 3.1). 350 300 250 200 150 100 50 0 1980

1985

1990

1995

2000

2005

2010

Figure 3.1 AUM of the managed futures industry in the years 1980–2013 (billion USD). Source: Author’s elaboration based on www.barclayhedge.com

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The Financialization of Commodity Markets

Table 3.1 AUM of managed futures by a subclass between the fourth quarter of 2014 and the second quarter of 2014 Assets under Management (USD billions)

June 2014

March 2014

Managed Futures industry

320.0

325.3

December 2013 331.2

Sectors Agricultural traders Currency traders Diversified traders Financial/Metal traders

1.03

1.12

1.37

19.52

20.48

21.36

198.78

194.02

198.69

84.25

82.58

81.04

Trading style Discretionary traders Systematic traders

17.26

18.61

19.88

284.64

278.03

281.46

Source: Author’s elaboration based on www.barclayhedge.com

Given the detailed breakdown of assets in the industry into markets of managed futures, it can be seen that in years 2013 and 2014, the majority of funds were invested in diversified portfolios. Among the applied methods of management, the automated transactional systems were strongly dominating (table 3.1).

Institutional Regulations of Managed Futures In this section, a presentation of the characteristics of the regulations of the managed futures industry is made. Due to the fact that this is a concept intrinsically related to the American market, and since CTA investments themselves are a subject of the American investment market, this chapter will be largely dedicated to the United States. The term “Commodity Trading Advisor” is usually used interchangeably with managed futures (also in this book) while, in spite of appearances, they are not exactly the same. By managed futures, we mean an investment category consisting of active opening positions on the market of futures contracts. CTA, in turn, is a legal category in the regulations of the United States, which, in accordance with legal requirements, must register with the Commodity Futures Trading Commission (CFTC). According to the definition by the National Futures Association (NFA), “A CTA is an individual or organization which, for compensation or profit, advises others as to the value of or the advisability of buying or selling futures contracts, options on futures, retail off-exchange forex contracts or swaps.”1 All companies in the United States that manage the money of their clients, using futures contracts, must register with the CFTC.

Active Investment Strategies in Commodity Markets

51

The history of the regulation of the futures markets is very long, and its origin dates back to the 1920s, when the Grain Futures Administration, which oversaw the agricultural futures markets, was established. It was at this time that the practice of partially delegating power to the industry through self-regulation—which is today regarded as an integral part of the institutional image of the futures market—first arose. In 1925, for example, the supervisory board of the Chicago Board of Trade received a mandate to independently determine the extent of the maximum price fluctuations over the course of a day. The CFTC was established by the US Congress in 1974. A few years later (in 1982), the NFA was established as a self-regulatory body, to which some powers were delegated. Currently, both institutions work in tandem to supervise both the futures markets and the institutions that invest in this area, within the United States. Duties of both institutions overlap in many areas. The NFA, with the support of the CFTC, supervises the entities associated with the NFA. The duties of the NFA include audits of the subordinate institutions—although the CFTC also has the right to conduct audits. The NFA also conducts a court of arbitration that adjudicates on disputes within the industry. In addition to CTAs, among the supervised entities, we should also mention the Futures Commission Merchants (FCMs), Introducing Brokers (IBs), and CPOs. Before moving on to further regulations in the CTA industry, it is worth characterizing the entities mentioned. Their definitions are presented in table 3.2. Table 3.2

Definitions of the CTA, CPO, FCM, and IB

CTA

Commodity Trading Advisor

A person, who, for pay, regularly engages in the business of advising others as to the value of commodity futures or options or involves in the advisability of trading in commodity futures or options, or issues analyses or reports concerning commodity futures or options.

CPO

Commodity Pool Operator

A person engaged in a business similar to an investment trust or a syndicate and who solicits or accepts funds, securities, or property for the purpose of trading commodity futures contracts or commodity options. The CPO either itself makes trading decisions on behalf of the pool or engages a CTA to do so.

FCM

Futures Commission Merchant

Individuals, associations, partnerships, corporations, and trusts that solicit or accept orders for the purchase or sale of any commodity for future delivery on or subject to the rules of any exchange and accept payment from extend credit to those whose orders are accepted.

IB

Introducing Broker

A person (other than a person registered as an associate person of a FCM) who is engaged in soliciting or accepting orders for the purchase or sale of any commodity for future delivery on an exchange who does not accept any money, securities, or property to margin, guarantee, or secure any trades or contracts that result therefrom.

Source: Author’s elaboration based on Jaffarian (2009, p. 172).

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The Financialization of Commodity Markets

At this point, it is worth defining exactly what the differences between the CTA and CPO are. The line of demarcation is directly related to the scope of activity. A CTA is an entity that manages and independently carries out transactions on behalf of investors, and it must be registered with the CFTC in this form. A CPO carries out other activities involving the accumulation of its clients’ funds into a common investment vehicle: a so-called pool. This may be, for example, a limited liability company or an investment fund. The received financial means are then allocated under the management of one or more CTAs (Jaffarian 2009, p. 172). According to the definition given in this chapter, a CTA can also become a CPO when, on its own initiative, it establishes a fund that collects the funds of investors; but funds-of-funds are classified as CPOs, not as CTAs. A comparison between the tasks and responsibilities of CTAss and CPOs is presented in table 3.3. A key indicator for the verification of the CFTC compliance status of a company is if it has investors from the United States. If the CTA or the CFO has investors from the United States, it must register with the CFTC and observe the relevant provisions. It should be emphasized that it is not important, for example, whether a company trades with futures contract in the stock markets operating in United States. If it trades in the United States but has no US investors, there is no obligation to register. The list of registered institutions is published on the CFTC website.2 Similarly, transactions in the futures market on behalf of US investors must be approved by the CFTC; however, such transactions on behalf of non-US investors, do not impose the obligation to comply with the regulations of the CFTC. It must be remembered that such an obligation may be imposed by local institutions and government agencies. For example, FSA regulates the trading of Table 3.3 Comparison of the tasks and responsibilities of CTA and CPO CTA responsibilities

CPO responsibilities

Developing trading strategies

Selecting CTAs and determining allocations to them

Monitoring performance and reporting to investors

Monitoring the performance of individual CTAs Monitoring pool performance and reporting to investors

Ensuring that funds and managed accounts meet the requirements of the CFTC and NFA

Ensuring that the pool meets the requirements of the CFTC and NFA

Ensuring that the investors meet all necessary requirements

Ensuring that the investors meet all necessary requirements

Complying with all rules and regulations of the CFTC and NFA

Complying with all rules and regulations of the CFTC and NFA

Source: Author’s elaboration based on Jaffarian (2009, p. 172).

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53

financial instruments in the United Kingdom, BaFin in Germany, and KNF in Poland. Despite the visible trend to regulate the futures markets better, in the area of operation of managed futures, there is still only a limited legislation; an example of this is the currency market. In any event, foreign exchange transactions in the United States went under the supervision of the CFTC in 1972, when the International Monetary Market (IMM) was created. Despite this fact, a large part of the trade is still done over-the-counter (OTC), and on the spot and interbank markets. The situation of contracts on stocks and stock indices, trading in which, in the United States, is done by two agencies—“trading” agency and “futures” agency—seems very interesting. These products are regulated jointly by the CFTC and the Security and Exchange Commission (SEC) (Jaffarian 2009, p. 171). The terms “CTA” and “CPO” refer to the legal forms of the investments in managed futures. However, when it comes to the actual investment vehicles, the two most common categories are managed accounts and funds. Both forms have their pros and cons. The accounts are managed in a similar fashion to the asset management services offered in Poland. They are advantageous in that they are intended to be relatively simple and inexpensive for the customer. The customer just needs to go to the FCM, complete all the formalities, negotiate fees and commissions, and transfer the relevant power of attorney to a desired CTA. Managed accounts are quite fluent, transparent, and remain under the full control of the investor (Jaffarian 2009, p. 172). The problem for investors in the case of these investments is a high entry threshold. It usually ranges from 10 to 20 million USD, which is an unbreakable barrier for smaller investors. If we look at the investment risk, more troublesome may be the fact that the legal owner of the account is the investor, so if a CTA generates a loss exceeding 100 percent of the funds in the account, the repayment obligation still rests on the investor. In case of investments through managed accounts, the customer funds are deposited in his own account through the FCMs, which, in a simplified way, are brokerage houses operating on the futures market. They are overseen by the CFTC and NFA and are audited by stock exchanges. The problem with the possibility of incurring losses in excess of 100 percent in case of investment funds is eliminated. Unfortunately, from a legal point of view, they are more complex products that require a private placement memorandum, annual audits of the financial statements, and the hiring of an external administrator whose task is to deliver reports to the investors. The costs associated with these services, depending on the scale of the fund, usually range from 10 to 50 base points. Among the advantages, in addition to the aforementioned limit on the maximum loss to the amount of capital invested, we should point out the benefits of joint funds. This allows the investors to have access to the managers

54

The Financialization of Commodity Markets

who place a high entry threshold, and who, because of the large amount of accumulated funds, present the opportunity to create a more diversified portfolio. As with investments through managed accounts, the customer funds are deposited in the fund administrator’s account; in this case the FCM acts as an intermediary. From the managers’ perspective, the investment fund presents a more preferable structure, and there are many advantages in its favor. First, the management of a joint fund is easier than managing many individual accounts. Second, this structure facilitates the allocation of positions, audits, calculation of fees and commissions, and so on. Third, in case of a fund, the manager can reveal a much smaller amount of information, so the chance of disclosing his investment strategy, and thus the loss of his valuable knowhow, is smaller. A significant increase in the popularity of managed futures occurred in the 1980s, along with the discovery of the value of diversification between different CTAs. Initially, the funds allocated money under the management of a single CTA, and they were most often created by this CTA. As the benefits of diversification have become better understood and documented (Irwin and Brorsen 1985), the assets managed by funds and funds-of-funds have grown. New developments in the market have increased the importance of CPOs that offer the investors the choice of CTA and allocation of the portfolio. The CTAs and CPOs registered in the United States can also offer investment (in the form of accounts or funds) to customers from outside the United States, on the same terms as domestic entities. In order to ensure tax neutrality, such entities are often registered abroad, in countries such as the British Virgin Islands, the Cayman Islands, and Luxembourg (Jaffarian 2009, p. 174). To summarize the considerations contained in this section, it must be stressed that there are three methods by which investors can gain exposure to managed futures investments (Kat 2005). The first method is the acquisition of participation units in a publicly managed futures fund, like in the case a traditional equity fund. The second option is to invest money through a CPO that collects money from investors and entrusts it to one or more CTAs. The third option is to entrust the funds directly to a CTA.

Managing Portfolios of Managed Futures Funds Over the years futures contract funds have developed their own specific methods of management of an investment portfolio. A general outline of these methods is presented below.

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55

Styles of Management of Managed Futures There are a number of distinct strategies used by managed futures funds. One of the broader divisions, into technical and fundamental strategies, is based on the type of the information used by a CTA. A fundamental investor is focused on discovering “true” value, while a technical investor is interested in the price movements existing in the market (Jaffarian 2009). Fundamental analysis seeks to determine the “intrinsic value” of the instrument—of a futures contract, for example—using financial, economical, and market variables. Examples of such variables may be statistical data on the economy, inflation, unemployment, or supply and demand associated with the given commodity. Fundamental analysis has many limitations. For example, market prices are also influenced by factors that the analyst is not able to accurately predict, which include changes in monetary policy, natural disasters (floods, storms), other disasters (wars, assassinations) or market rumors, and the impact of liquidity on the market. On the other hand, the technical analysis focuses on price movements. Technical analysts examine the track record of the futures using quantitative techniques to try to locate recurring patterns. Technical analysis is based on the assumption that although the prices reflect all available economic information, the market is governed by recurring patterns that allow the generation of profit. These patterns—for example, on the currency market—can arise from, among others, uneven economic growth or gradual changes in the monetary policy (Jaffarian 2009). A technical analyst has no need to verify all the factors underlying price changes; he is only interested in movements in the market. Focusing on quotations makes technical analysis applicable to a number of different instruments, from commodities, through stocks and bonds, up to currencies. The second most common division by the adopted strategy refers to the analytical process, which leads to the generation of trading signals. We mean here the discretionary and systematic strategies. Discretionary investors monitor fundamental and technical market situations and, based on their own judgment, they take individual decisions on the conclusion of the transaction. On the other hand, systematic investors rely on a set of precise rules laid down in advance, called a transactional system, that automatically generates signals to buy, sell, change positions, etc. Such a system is often referred to as a black box (Narang 2009). In contrast to discretionary investments, there is not much room here for individual decisions taken by the manager, whose functions in the investment process are design, monitoring, and modifying the automatic investment system. Although there are CTAs employing both discretionary and systematic strategies, the dominant model, by far, is a systematic approach. A discretionary approach, in turn, is usually associated with global macro funds, because the managed futures industry used to be a priori regarded as a

56

The Financialization of Commodity Markets

kind of automated transactional system. For this reason, a further part of the characteristics of the investment process of CTAs will focus precisely on a systematic approach.

Trading System Trading systems operate on the basis of automated computer algorithms, so they are often referred to as computer-based or model-based. A typical trading system includes a set of strict rules that specify when to sell and when to buy a contract. Exceptions to these rules are, in principle, not allowed, except for modifying the entire transactional system. The algorithms are based on the recurring market patterns that have been discovered based on analysis of track records. The process of verifying both the effectiveness of certain rules and the possible use of recurring patterns is called backtesting. Backtesting is based on a large, albeit limited sample of historical data. Technical analysts, of course, try to use the widest range of market events to check system responses to various possible variants of future market behavior. Thus, the analyst assumes that the market will behave in the future as it did in the past, which is not always true. Previous studies on technical analysis have not led to a clear conclusion about its effectiveness (Till and Eagleeye 2004), which will be discussed in the subsequent chapters. The individual managed futures funds typically use more than one transactional system; during the lifetime of the fund, the existing systems are subject to various modifications—sometimes a new model is added, sometimes some of the existing ones are removed. Individual systems do not necessarily have to be based on very different principles; they may, for example, be based on a different time horizon. For example, a fund may operate on two systems: a long-term system and a short-term one, and investment strategies may be based on the signals generated by both systems. This is one of the elements that should be kept in mind when investing in managed futures funds, because it shows that historical results are not necessarily an indicator of returns in the future. The process of constructing a trading strategy usually involves a number of issues. Among them are four consecutive key elements (Till and Eagleeye 2004): design, calibration and validation of the transactional system, structure of the portfolio, as well as risk management and leverage. Individual elements with examples will be discussed below. Designing the Trading System According to Jaffarian (2009), the transactional systems existing in the market can generally be divided into three groups (figure 3.2): systems based

Active Investment Strategies in Commodity Markets

57

Managed futures strategies

Trend following

Moving averages

Break-out

Non-trend following

Relative value

Reversal

Pattern recognition

Figure 3.2 Division of futures contracts investment strategies. Source: Author’s elaboration.

on trend-following strategies, systems based on non-trend–following strategies, and systems based on relative value strategies. Within each category, there are further divisions. Most of the strategies can be classified as moving average strategies or breakout strategies, and non-trend–following methods are, in general, divided into countertrend strategies and strategies based on pattern recognition. Trend following Strategies Trend following strategies have definitely dominated among the strategies used by CTAs (Fung, Hsieh 1997a,b). Funds that use this strategy type try to “catch” trends and earn money, by entering into transactions in accordance with the current trend in the market. When the trend is upward, the trend following funds will seek to hold a long position, whereas, when the trend is downward, they will try to take a short position. As the name implies, these investors do not try to predict future price movements, but just “follow” them; they wait for trend formation and then they perform the appropriate transaction. In order to identify the trend, all transactional systems make use of the indices that compare current price levels with historical ones. Moving average strategies use, for this purpose, a smoothed series of historical prices. When the current prices or the moving average are above a certain level, which can also be a function of moving averages, a long position is taken, and in the opposite situation—a short one. The breakout systems operate in a different way; they wait for a new trend to crystallize, looking into the range of historical price fluctuations. When the price grows (or falls) significantly beyond the range of historical price movements, it is seen as the emergence of a new upward (or downward) trend and a long (or short) position is taken.

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The strategies based on moving averages have gained significant popularity, because the averages are statistically quite well defined, understandable, and easy to implement for testing transactional systems. In the simplest terms, the arithmetic average is calculated as a balanced arithmetic average by formula (3) (Lhabitant 2008a). MAN =

k 1 ∑ Pt N t = k − N +1

(3)

whereby N represents the number of periods used for the calculation of a moving average, k is the relative position of the current period in the total number of the analyzed periods, and Pt is the price of the instrument at the time t. The average is weighted, so any historical price occurring in recent periods will hold equal importance in the calculation. The moving average may be based on periods of different lengths, such as days, minutes, or individual ticks. We may have, for example, a 20-day moving average. In calculation of the average value, the “oldest” price, older than 21 sessions, will be dropped each day, and a newer price, from the previous session, will be added. The moving average has the advantage that it smoothes the market time series. If the market is generally characterized by an upward trend with lower prices occurring occasionally, the moving average would “dampen” the noise and allow technical analysts to recognize the current trend. As mentioned earlier, the moving average does not anticipate changes in the market, but—as a result of its consideration of historical quotations—it is a lagging indicator; the moving average systematically follows the market price. This effect is visible after overlaying the price graphs of a given instrument on the moving average. To illustrate the issue, figure 3.3 shows sample quotations with a designated 20-session moving average. The moving average effectively “captures” the trend while simultaneously smoothing the quotations. In a growing market, the moving average is below the current quotations. This is due to a lagging mechanism embedded into the average, which makes the average still show an “old” trend. In turn, in a declining market, the average will be higher than current market prices. This relationship implies one of the most common signals of sale/purchase in technical analysis. If the intersection of the average represents the trend change, the rule reads as follows; buy when the market price crosses the moving average from below to above, and sell when the market price crosses the moving average from above to below. Figure 3.4 shows buy and sell signals on a sample chart. Moving averages can be calculated on the basis of the periods of various lengths. Moving averages based on short periods follow the market more closely. As a result, although they allow for faster trend identification, they also generate more buy and sell signals, of which a large proportion is wrong, and this, in turn, is associated with higher transaction costs. This effect can be particularly troublesome during side trends, when there is

4,900 4,700 4,500 4,300 4,100 3,900 S&P-GSCI TR Index 20-period moving average

3,700

10 4,

20 N

Se

ov

pt em

em

be

be

r0

r0

4,

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20

10

0 01

0 01 ,2 04

04 ar ch M

Ja

nu

ar y

04

,2

,2

01

01

0

0

3,500

Figure 3.3 Moving average—an example. Source: Author’s calculations.

2900 2800 2700 2600 2500 2400 2300 2200

Prices 20-period moving average

2100

0 be

ec D

em ov N

em

be

r0

1,

20 1

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pt

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st gu

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01

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01 ,2 01 0

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2000

Figure 3.4 Sample buy and sell signals based on moving average. Source: Author’s elaboration.

The Financialization of Commodity Markets

60 50 45 40 35 USD

30 25 20

Prices 20-period moving average 40-period moving average 60-period moving average 80-period moving average 100-period moving average

15 10 5 0 January 02, 2008

January 02, 2009

January 02, 2010

Figure 3.5 Comparison of moving averages. Source: Author’s elaboration.

no upward or downward trend in the market. However, long-term moving averages synthesize more historical prices, so they are less responsive to current changes of prices in the market (figure 3.5). As a result, they generate fewer false signals, but they can lead to missed investment opportunities. Figure 3.5 shows calculated 20-session, 40-session, 60-session, 80-session, and 100-session averages of sample quotations. Unfortunately, there is no definitive answer to the question, what moving average should be used. However, market practice shows that main long-term trends are well illustrated by a 40-week (200-session) average, medium-term trends by a 40-session average, and short-term movements by a 20-session or less (Lhabitant 2008a). System optimization is one of the key tasks facing managed futures funds. Length of the employed moving average also depends on the nature of the market, its variability, existing cycles, etc. Other issues that need to be addressed are, for example, which prices (closing, average, maximum, minimum, opening, etc.) the system will base on or what threshold must be exceeded above (or below) the average in order to generate a buy (or sell) signal. It is worth noting that most basic principles—on which the transactional systems are based—are relatively simple. One should be aware of the details and long calibration times required in order to adjust the rules to the specifics of individual markets so that, at a later time, the system can operate seamlessly, without the interference of the author. Classical arithmetic average is just one of the variants of moving averages, which can be computed in many ways. Recently, for instance, the exponential moving average has become increasingly popular; it depends mostly on more recent price movements as compared to the arithmetic average. The

Active Investment Strategies in Commodity Markets

61

exponential moving average is usually calculated according to formula (4) (Jaffarian 2009). EMAt (λ) = λ Pt + (1 − λ)EMAt −1 (λ), 0 < λ < 1

(4)

The exponential moving average is a weighted average of the current price and the value of the exponential average from the previous calculation period. In formula (4), λ represents a parameter deciding what weight should be assigned to the current price and what should be assigned to the historical one. Due to the fact that λ is always less than 1, the importance of individual prices in the calculation period will not be equal; the earlier prices will be less important. Trading signals generated by the exponential average are constructed much like the arithmetic average. At this point, it is worth mentioning that these signals may be more complex than ordinary penetration of the average by market quotations. The most popular trading signals operating in the industry include (Jaffarian 2009): ●●

●●

●●

upward intersection of the moving average or a series of moving averages by market prices (buy signal) or analogical downward intersection (sell signal) upward intersection of the long-term moving average by a short-term moving average (buy signal) or analogical downward intersection (sell signal)3 arrangement of series of moving averages in ascending order (buy signal) or analogical arrangement in descending order (sell signal)

Figure 3.6 shows the signals generated by the intersection of two moving averages: a short-term 20-session average and a longer 80-session average— for sample quotations. When the short-term average crosses the moving average from below, a buy signal is generated, while, when an opposite intersection arises from above, it is read as a sell signal. As a result of the application of this strategy, first, an effective signal to take a short position and then a profitable signal to take a long position are generated. However, in the first half of 2010, the market entered a side trend that led to a series of false signals that did not generate profits, but instead incurred transaction costs. The tools used by the managed futures industry are constantly being improved, so it is worth mentioning at least a few modifications to the usage of moving averages (calculation by Lhabitant 2008a). Fixed length moving averages (FMAs) operate in a similar manner to systems with Variable Moving Averages (VMAs), except that the transactions are usually made for a predetermined fixed period. The purpose of this modification is to avoid the risk of reversing the trend. The adaptive moving averages (AMAs), in turn, are based on the premise that short-term moving averages adapt better to market conditions; whereas

The Financialization of Commodity Markets

62 180 160 140

USD

120 100 80 60 Prices 20-period moving average 80-period moving average

40 20

Figure 3.6

10

0

20 5, r0 be

ay

N

ov

em

M

r0 be em ov

05

5,

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20

01

09

9 00 ,2 05 ay M

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N

ov

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M

be

ay

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05

5,

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20

00

08

8

0

Intersection of moving averages as a transactional signal.

Source: Author’s elaboration.

when trends are visible and the market is in consolidation mode, long-term averages are more appropriate. The AMA systems try to capture the current state of the market and then match it with the moving average. Yet another category, high-low moving averages (HLMAs), are systems consisting of two moving averages, one of which is calculated based on the maximum prices in the analyzed subperiods, and another one based on the minimum prices. The two averages calculated in this manner never intersect, but can be used as, for example, support and resistance signals, which, when broken out of, mean that the trend has changed. Some traders use them also as zones within which positions toward a dominant trend are built. The triple moving averages (TPAs), which use as much as three moving averages, are also quite an interesting category. If the medium-term average crosses the long-term average and, additionally, the short-term average crosses the medium-term average, a buy (sell) signal is produced (depending on the direction of the crossover). Breakout strategies arise from a slightly different philosophy, but they are also based on trend following. These systems “watch” the scope of changes in market prices and then conclude contracts for long positions (upside breakout) or for short positions (downside breakout). A simple example of breakout strategy is the look back strategy. These systems observe 20 sessions back and record the maximum and minimum prices of this period. If the current price exceeds the maximum from the last 20 sessions, it is considered a buy signal, whereas if the price drops below the minimum from those 20 sessions, it is a sell signal.

Active Investment Strategies in Commodity Markets

63

80 75 Sell 70

USD

65 60

Buy

55 50

Buy

45

10 20 4,

20

r0

4, em ov N

Se

pt

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10

0 ,2

,2 04

,2 04 ch ar M

01

01

0 01

0 01 ,2 04 y ar nu Ja

0

40

Figure 3.7 Breakout strategies for sample quotations. Source: Author’s elaboration.

Graphic illustration of the breakout systems is shown in figure 3.7. Initially, in the period January–February, the price index moved in a narrow price channel. Breakout of the maximum cap, also called a resistance level, meant a buy signal that allowed generating profit. Then, after a few sessions of intensive growth, the market once again returned to consolidation, which ended in May with a sharp upward breakout. Another side trend—this time a longer and wider one—lasted until September 2010 and ended with the breaking of the range minimum. Breakout of the lower limit, also called the support level, is usually explained as a sell signal. Also, in this case, it allowed for predicting a significant drop in prices. A trend following strategy as a method of investment decision making is not free from defects (Lhabitant 2008a). First, the strategies of this type are “slow,” which is well illustrated by the moving averages example. Due to their nature, moving averages produce a signal for taking the position some time after the trend formation, and a signal for closing the position some time after its reversal. In other words, the investor must always be prepared to take the position with a delay, losing part of the movement, and then also to close the position with a delay, losing part of the generated profit. This feature characterizes all lagging indicators in technical analyses, and the CTAs are aware of it. Nevertheless, the investors—who can see the loss of part of the profits generated earlier—tend to feel some psychological discomfort. The second most important limitation arises from the nature of

64

The Financialization of Commodity Markets

the trend following strategy, which needs trends to generate profitable trad­ ing signals. This means that the rates of return cannot move randomly, but must be characterized by a positive autocorrelation. It is not surprising that, in the absence of the autocorrelation of rates of return, the trend following systems, including those based on lagging indicators, fail and, instead of generating profits, only produce false signals and transaction costs. A rem­ edy for the ill may be found in the second group of trading systems; that is, the non-trend following strategies. Non-trend follow ing Strategies The goal of non-trend following strategies is to find opportunities to make a profit in the markets in which no clear trends are visible. In practice, most systems of this class are classified into one of two categories: pattern recog­ nition techniques and countertrend strategies. The first group—pattern recognition strategies—aims to find recurring patterns on the classical or candlestick graphs and, based on these patterns, tries to forecast future price movement. One of the most recognizable pat­ terns is the “head and shoulders” (Murphy 1999). An example of this pat­ tern is presented below (figure 3.8).

Figure 3.8

Head and shoulders pattern on the sample graph.

Source: Auth or’s elaboration.

Active Investment Strategies in Commodity Markets

65

Head and shoulders is a formation consisting of three consecutive peaks, of which the central one is the highest. The line connecting the base outlined with each peak in the graph is called the neckline. When the graph has already formed the right shoulder of the formation, and then the neckline has been exceeded, according to the rules of the technical analysis, it is the harbinger of a coming decline. The height of the head, that is, the distance from the top of the highest (central) peak to the neckline, also defines the minimum scope of a decline. Head and shoulders is an example of a formation that can be read on the classic line graph, though recently, the patterns based on candlestick graphs have become increasingly popular. To their advantage is that they are easier to test on historical data. Candlestick charts, allegedly having their roots in the Far East (Nison 2001), show the quotations in the individual periods using the so-called candlesticks, which is explained by figure 3.9. Each candlestick consists of a body and wicks, also referred to as shadows. The body may be bright or dark, depending on whether the session of the given day is upward or downward. A bright body means that the quotation of the day ended up at a higher price than when it started, and the lower and upper limits of the body show the opening and closing price respectively. A dark body suggests a downward session, that is, a situation in which the closing price (the lower limit of the body) is lower than the opening price (the upper limit of the body). It is worth noting that in extreme situations, when the opening price is equal to the closing price, a body may actually not exist and be limited to just a horizontal line. Two wicks (shadows) usually grow from the body, a lower wick and a higher one, which indicate the maximum and minimum prices in a given period. In special situations, the wicks may also not exist. This happens when, for example, during an upward session the closing price is equal to the maximum price that day.

Maximum price

Closing price

Opening price

Opening price

Closing price

Minimum price

Figure 3.9 Shape of Japanese candlesticks. Source: Author’s elaboration.

Maximum price

Minimum price

The Financialization of Commodity Markets

66 5.25

5.20

5.15

5.10

5.05

,2 01 0

Ju

ly

16

,2 01 0

Ju

ly

15

,2 01 0

Ju

ly

14

,2 01 0

Ju

ly

13

,2 01 0

Ju

ly

12

,2 01 0

Ju

ly

09

,2 01 0 08 ly

Ju

Ju

ly

07

,2 01 0

5.00

Figure 3.10 Bear market truncation on the sample graph. Source: Author’s elaboration.

Candlestick patterns consist of a sequence of specific candlesticks. An example is the formation of a bear market truncation shown in the chart below (figure 3.10). Bear market truncation consists of two consecutive candlesticks, the first of which has a bright body (upward session) and the other, a dark body (downward session). The necessary condition is that the dark candlestick entirely covers the bright one. In other words, the opening of trading on the second day of shaping the formation must occur above the previous day’s closing and end below the opening of this session. Bear market truncation is the occurrence of a change from an upward trend to a downward trend, and thus, it signals the opening of a short position. The latter of the aforementioned groups of non-trend following strategies are the countertrend strategies. These systems use different types of indicators, such as oscillators, in order to identify the current range of price movements in the market before generating buy signals when prices are moving around the bottom of the range, or sell signals when quotations are moving close to the top. One of the less complex countertrend indicators is the Relative Strength Index (RSI) (Welles Wilder 1978). The RSI is calculated by formula (5): RSI = 100 −

100 U 1− n Dn

(5)

Active Investment Strategies in Commodity Markets

67

whereby Un represents the average change in prices during the upward periods within recent n periods, and Dn the average change in prices during the downward periods within the same period. Although the RSI can be calculated for various periods (minutes, hours, weeks) and for different numbers of these periods, however, in practice, the most common is the RSI calculated for 14 days. RSI is an oscillator, which means that its values oscillate within a precisely defined range. The range within which the RSI can move is from zero to 100, with 50 as the neutral level. The upper ranges of the oscillators are interpreted as an overbought market condition. According to the rules of technical analysis, they are interpreted as a situation where the prices have increased so much that there is a high risk of them falling. Conversely, their lower ranges indicate an oversold condition, which may signalize increase. The exact boundary where the neutral zone ends and overselling or overbuying begins is, of course, conventional. In practice, for the RSI, a range from zero to 30 is most often assumed to be an overselling zone, the neutral zone ranges from 30 to 70, and from 70 to 100 indicates an overbought zone. Trading signals relate mostly to indicators in excess of the limits of the ranges described above. A typical transactional system might look like this: Buy when the indicator breaks the border of the oversold zone from below and turns back to the neutral zone, and sell when the indicator breaks out of the border of the overbought zone from above and also turns back to the neutral zone. An illustration of these trading rules is shown in figure 3.11, drawn up based on sample quotations. The above example shows the nature of the countertrend systems quite well. On the one hand, they are reliable during a side trend and within a stable range of fluctuation. This applies especially to the first half of 2010 in the graph shown above. On the other hand, the oscillators fail, when there are clear trends visible in the markets, hence a lot of false signals may be seen in the second half of 2010. 180 160 200

140

100 RSI (LHS)

80

100 60 40 20 0 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2009/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/… 2010/…

0

Figure 3.11 RSI oscillator for sample quotations. Source: Author’s elaboration.

Price

120

Prices (RHS)

The Financialization of Commodity Markets

68

Jaffarian (2009, pp. 183–184) emphasizes two aspects of the countertrend systems that are worth making a note of. First of all, these strategies are usually optimized to operate on much shorter periods of time than the trend following techniques. While the average duration of an open position in the latter case lasts from a few to several months, the countertrends operate on periods limited to a few days or even minutes. Previous studies have shown that for a longer time horizon of a single transaction, the countertrend systems simply do not appear profitable (Miffre and Rallis 2007). One of the consequences is a much higher turnover in the non-trend following systems. The trend following funds generate the volume of transactions at the level of 1000–2000 contracts per 1 million USD of AUM annually, while the managers of the non-trend following funds usually make 5000 transactions a year per 1 million USD of assets (Jaffarian 2009, p. 184). Relative Value Strategies Relative value strategies seek to exploit deviations from the structural dependencies and correlations underlying the operation of the various financial markets. These strategies assume that the instrument quotations react to the information published in an inefficient way. These inefficiencies can be identified by deviations from the typical correlation between the instruments, and can then be used to make a profit. Therefore, relative value strategies are a kind of quasi arbitrage. A chart provided below (figure 3.12) shows quotations of two futures contracts, A and B, the former of which demonstrated significantly higher rates of return in the period from 2005 to 2007, and significantly lower 350 Instrument A 300

Instrument B

250 200 150 100 50 Sell A, buy B

0 01 ,2

y

y

Ju l

Ju l

04

,2

00

8 y Ju l

04

,2

00

7 y Ju l

04

,2

00

6 y Ju l

04

,2

00

5 00 ,2 y Ju l

04

9

Buy A, sell B

04

Buy A, sell B 0

Figure 3.12 Relative value strategies. Source: Author’s elaboration.

Active Investment Strategies in Commodity Markets

69

ones in the period from 2007 to 2009. A technical analyst with the appropriate indicator (with a modification of the previously described RSI, for example) could identify deviations from the long-term dependence in 2007 and open a short position. Then, this position would be reversed after the convergence of prices two years later and in this way, the investor would achieve a profit. The strategies described above—trend following, non-trend following, and relative strength—can handle most transactional systems used by the managed futures industry. The described examples were fairly simple, although it is worth remembering that the strategies used in practice are more complicated due to the long-term calibration and use of various types of filters, rules of output, rules of input, as well as rules for determining position size and risk management.

Calibration and Validation of the System Before the system can be used to manage assets (funds) in practice, it is backtested. The rationale behind this procedure is the fact that if an investment rule is not reliable for the past, the chance that it will make a profit in the future is low. For this reason, backtesting is the essence of the activity in the managed futures industry. The funds regularly verify their own investment systems on different data, markets, and periods. The testing actually never ends because, on the one hand, the market constantly provides new data, and on the other hand, the constant changes in transactional systems require constant validation. What’s more, if an increasing number of funds try to use a similar scheme in the market, it can stop operating completely or start operating in a different manner. As a result, a system that was effective for the past 15 years might incur losses for the next 15 years. Therefore, validation and testing of systems continue uninterruptedly. From an investor’s perspective, it should be kept in mind that the transactional systems tested on historical data that is presented to them—for example, in marketing materials prepared by the funds and their managers— usually look very favorable. In practice, they can hide much of the biases that distort the results of the backtesting, thus overstating returns and downplaying the risk (Lhabitant 2008a, pp. 402–403). An example might be a pretest bias. The investment rules, which become the inspiration for transactional systems, typically stem from an observation of the quotation history and the personal experiences of the analyst. Where the strategy is formed based on the observation of the historical quotations, it is likely that the system tested on the same data will show positive results. However, the fact that prices were characterized by the same pattern in the considered period might just be a coincidence. Another problem might be excessive data mining. In its extreme form, the analyst can browse tens of thousands of trading rules and calibrate them to market data. It is obvious that in such a situation, at least some rules

70

The Financialization of Commodity Markets

generate profits, even if this might be a coincidence. The more such possibilities are tested, and the more extensive the calibration of the system is, the higher is the risk of overfitting historical data. Also, the trading costs bias is worth mentioning here. When testing the transactional systems, analysts often make simplistic assumptions about trading costs, which ignore, for example, the spreads between the buy and the sell prices, the importance of liquidity in the market for transaction cost, and the variable size of the margin deposits. This may greatly inflate the profits from the system, especially for strategies that require frequent transactions in highly variable or illiquid markets. The broadly meant trading costs are also related with the slippage control effect. Many tests based on historical data assume, for simplicity, that the transactions are carried out at closing prices. This is not entirely true, because the CTAs must take into account the so-called slippage costs, that is, the difference between the price at which the order is made and the price at which the transaction is to be made. In practice, it should be assumed that the amount of transaction costs is directly proportional to (among other things) the volume of transactions and the size of AUM, and inversely proportional to the liquidity of the market. Interesting discussion on this topic can be found in the publications by Perold and Salomon (1991), Korajczyk and Sadka (2004), Vangelisti (2006), Narang (2009, pp. 67–78), and Serbin, Bull, and Zhu (2009). Managers are usually aware of these “slippage costs,” so they take into account the type of orders, time and manner they are placed in, and what market actors take similar positions; but these elements are rarely taken into account in the backtesting. Using the information that is not yet available (that is look-ahead bias) is an important and recurring issue. In the validation process, some trading systems are based on data that was not available at the time of the simulated transaction; since while they indeed were published on the same day, it was only after the session has been closed. Sometimes the look-ahead bias may take a more covert form. Looking for a moment beyond the futures market, we can find a fairly simple example in the stock market. When testing a system for the stock market based on a low P / E trader index, by rebalancing the portfolio in January, the system may calculate the indices based on results of companies for the previous fiscal year although they are only to be published a few weeks later. The biases described above may produce unreal results in the validation process that would not be repeated in the future. Therefore, it is important to check that the system operates under different conditions and in different periods. It should also be remembered that the traders often have a natural tendency to underestimate market volatility and to see patterns and schemes where they do not exist (Hirshleifer 2001). In addition, they are also usually overconfident in their forecasting skills. What’s more, the higher their overconfidence, the lower is their ability to predict the future (Griffin and Tversky 1992).

Active Investment Strategies in Commodity Markets

71

Even if the system has been properly validated and the tested rules seem to augur well, there still remain at least two things the CTAs should take into account (Jaffarian 2009, p. 177). First, they need to consider how soon other investors would be able to discover the same pattern in the market and begin to use it. This may increase the pace at which the transactional system gradually degrades over time. Second, it is worth noting that most of the systems are based on a narrow market niche or on an inefficiency, which has its limits. Once a strategy exceeds a certain level of AUM, it may start to operate less effectively or become completely inoperative. A famous exemplification here is the Amaranth Advisors fund, where the scale of position it had taken was the primary reason for its fall (Till 2006a; Gupta and Kazemi 2007; Till 2008; Chincarini 2008). This fund committed more than 50 percent of its assets to the natural gas market of the United States and controlled a volume equal to one month’s consumption of gas in the country. As a result, it almost completely lost the ability to control the risk and liquidity of its portfolio.

Structure of the Portfolio Contrary to appearances, most CTAs and futures contract funds are not confined to a single transactional system but use many of them at the same time, on multiple markets. The diversity of systems may rely not only on various time horizons (long-term and short-term systems), but also on different investment philosophies (e.g., non-trend following and trendfollowing strategies). As a result, the manager exploits the effects of diversification between various markets and various strategies, which is aimed at reducing the risks associated with individual contracts. Due to low correlation, profits and losses on individual positions are largely averaged out by the end of each session. At this point, it is worth mentioning that, in comparison with traditional equity funds and bond funds, managed futures have greater diversification features. In major funds, where computer systems are used on a large scale, applying a strategy in more than 100 markets at the same time is not uncommon. What’s more, these are rather diverse markets: from commodities, through stocks, bonds, and currencies, up to interest rates; so their correlation is not high. In effect, the benefits of diversification within a portfolio of diverse futures contracts are greater than the benefits of an independent stock portfolio. To better illustrate this effect, figure 3.13 presents the risk of portfolios made up of from one to ten randomly selected instruments from two baskets: a stock basket based on US large-cap stock and a futures basket (underlying instruments: Nikkei, S&P500, gold, copper, Brent crude, wheat, USD/JPY and CHF/EUR currency pairs, long-term US Treasury bonds, rates, three-month EURIBOR). Correlations were calculated based on the monthly rates of return from the period August 2005–September

The Financialization of Commodity Markets

72

Standard deviation of returns

45% Large-cap stocks Futures contracts

40% 35% 30% 25% 20% 15% 10% 5% 0% 1

2

3 4 5 6 7 8 Number of instruments in a portfolio

9

10

Figure 3.13 The diversification of portfolios of futures and stocks. Source: Author’s calculations.

2010. Standard deviation in the rates of return has been assumed as a risk measure. As suggested in figure 3.13, portfolio diversification involving futures contracts leads to a faster and stronger decrease of risk than in the case of a stocks-only portfolio. It should be noted that the above example is limited only to long positions, but managed futures are not limited to these positions and this leads to an even lower correlation between individual instruments. Similar conclusions about the benefits of diversification have been drawn by, among others, Till (2000a). Portfolio construction is always done according to a set of rules. Managers have quite a lot of flexibility, because derivatives, which futures contracts are, do not require the involvement of the full value of the assets; only a security deposit. The simplest rule is a balanced portfolio in which an equal amount is allocated to each distinct position. This involves the necessity for periodic rebalancing because the underlying instruments quotations will grow and fall, and thus they will change the values of the positions taken. A more advanced approach uses the concept of risk capital. In other words, the share of a position in the portfolio may also depend on its volatility. For example, if the analyst takes into account two markets, one of which has a greater standard deviation of return rates while the other bears a smaller one, then the share of the former will be correspondingly smaller and the share of the latter will be larger, so that equal risk is allocated to each market. Position variability may be associated with setting stop-loss orders that limit the maximum loss. The volume of the transactions made may also take into account the correlation of the new position with the existing portfolio, and its impact on the overall risk (Lhabitant 2004, pp. 315–319).

Active Investment Strategies in Commodity Markets

73

Risk and Leverage Management Risk management is one of the key elements of the design of investment strategies (and according to some authors, the most important one (Eagleeye 2007)), since the fund has to construct a portfolio to fit it to the risk expected by the investor. Risk management is carried out at several levels. Most funds use several investment strategies and calculate the following measures of risk for them (Till and Eagleeye 2004, p. 285): ●● ●● ●● ●● ●●

value at risk (VaR) based on the recent volatility and correlation the greatest loss in “typical” conditions the greatest loss in extreme conditions contribution to the VaR of the entire portfolio contribution to the loss of the portfolio in extreme conditions (event risk contribution)

The last two are the most important measures, which indicate whether the investment strategy reduces or increases the risk of the entire portfolio. In global terms (the entire portfolio), the risk is usually calculated using the following indices: ●● ●● ●●

VaR based on the recent volatility and correlation the greatest loss in the “typical” conditions the greatest loss in the defined special conditions

Each of the above measures should be regularly monitored and compared with the investor’s ability to pay and their willingness to take risks. For example, if the investor is not able to accept at any time a loss greater than 10 percent, then all three measures should not exceed 10 percent. If this happens, a solution may be, for example, a macro-hedge leading to the largest possible exposure of the entire portfolio. Another element of risk monitoring is to check the portfolio’s behavior in extreme conditions, that is, stress tests. Till and Eagleeye (2004, p. 285) suggest that the tests should cover at least the following periods: the crash in October 1987, the Gulf War in 1990, the Russian crisis in 1998, and the attack on the WTC in September 2001. The portfolio that does not meet ideal expectations in these periods may not be acceptable to clients who are looking to diversify their instruments into alternative investments. The third issue, which is particularly important to the risk management process in the managed futures industry, is the level of leverage. An inherent feature of futures contracts is that they require a minimum commitment of capital in connection with the small investment required. As a result, the volume of individual transactions and the desired level of

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74

exposure to individual asset classes is not so much a result of how much available capital the manager holds, but also of how much risk he is willing to take. The adopted leverage level is a design element of the transactional system. The market comprises both the CTAs characterized by small fluctuations and those who are able to bring a 40 percent loss one year and a 100 percent profit next year. It is important that the leverage level is fitted to the expectations of investors. Many funds even offer their customers several options of equal transactional systems, but with different levels of leverage. In addition, leverage is so important that, as some studies indicate, without using it, the profits made out of the managed futures industry would be relatively low. This is possible only by using substantial leverage, which is even higher than in most classes of hedge funds (Till and Eagleeye 2004, p. 288). Table 3.4 shows the rate of return on hedge funds and managed futures in the period from 1997 to 2001, both before and after adjustment of the level of leverage. The table reveals the crucial impact of leverage on the achieved results. The situation has been pretty well summarized by Bruce Cleland, from the renowned Campbell and Company, in the pages of “Risk Magazine” (Patel 2002). Bruce Clendel is one of the pioneers of the managed futures funds. The long-term rate of return earned by the Campbell funds over 31 years was 17.6 percent, net the fees. “No market-place is going to be so inefficient as to allow any kind of systematic strategy to prevail over that period of time, to that extent. Our true edge is actually only around 4% a year, but

Table 3.4 Levered and delevered returns by hedge fund strategy, 1997–2001 Style

Average levered return (%)

Average delevered return (%)

Short-biased

13.7

9.3

Global macro

16.8

8.9

Emerging markets

16.9

8.8

Event-driven

14.7

8.3

Merger arbitrage

14.7

7.0

Long/Short equity

14.0

6.3

Fixed income

9.6

4.8

Convertible arbitrage

10.6

4.2

Managed futures

10.5

4.2

n/a

n/a

Distressed securities

Note: Analysis of financial leverage was made using five-year historical leverage data and performance from the following databases: Altvest, CSFB/Tremont, EACM, HFR, Institutional Investor, and CMRA. Source: Rahl (2002, slide 52).

Active Investment Strategies in Commodity Markets

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through leverage of between 4–1 and 5–1 you are able to get a much more attractive return”—Cleland commented.

Theoretical Grounds for Technical Analysis In most cases, the methods of managing futures funds are inextricably linked to the technical analysis. Understanding its principles and basics is crucial to study the potential impact of financialization of markets on the effectiveness of managed futures funds. Therefore, we will study the theoretical foundation on which technical analysis is based in this book. Technical analysis has always been a rather controversial method of studying financial markets (Irwin and Park 2007; Park and Irwin 2004; Schneeweis, Kazemi, and Spurgin 2008). It is difficult not to see a gap between its popularity among market practitioners and the skepticism of the academic community. Technical analysis as a method of testing the commodity markets has a very long history. For example, “Japanese candlesticks,” which appeared among the tools of Western European and American investors as late as in the 1970s, had already been used in the Far East two hundred years earlier (Nison 2001). Now you can find dozens of books devoted to transactional systems in the book market (e.g., Peters 1994; Gately 1995; Schwager 1995; Arms 1999; Etzkorn 1997; Gately 1998; Bensignor 2000, etc.), and technical analysis itself had already been a big hit in 1965 when, in the United States, a pioneering survey of individual investors in the futures markets was carried out. It was found that 53 percent of the respondents relied exclusively upon technical analysis (Smidt 1965). A little later, in 1985, the Group of Thirty (1985) asked the institutional market players a similar question. In the sample group comprising 40 banks and 15 brokerage houses across 12 countries, 97 percent of the banks and 87 percent of the brokerage houses found that technical analysis had a significant impact on the market. This method is also popular among the brokers : according to various studies, about 30–90 percent of the people working in this profession are guided by this method (Frankel and Froot 1990; Taylor and Allen 1992; Menkhoff 1997; Lui and Mole 1998; Cheung, Chinn, and Marsh 2000). What’s more, technical analysis is particularly important in the sector of CTAs. According to Billingsley and Chance (1996), 60 percent of all CTAs rely solely or heavily on automated technical trading systems. In contrast to the stock investors, the academic community is rather restrained toward technical analysis. According to Irwin and Park (2008, p. 910), this may be related to at least two issues. First, several attempts to verify the technical analysis were made, which resulted in the rejection of the thesis of its usefulness (Fama and Blume 1966; van Horne and Parker 1967, 1968; Jensen and Benington 1970). Second, it is contradictory to the

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The Financialization of Commodity Markets

Efficient Market Hypothesis (EMH). According to the EMH, the market is efficient when prices always fully reflect all the available information (Fama 1970). Jensen (1978) made a further division of EMH with the scope of the information θt, taken into account into weak, semi-strong, and strong forms. 1. A weak form of market efficiency implies that the information θt covers only the history of the prices in a given market up to time t. 2. A semi-strong form of market efficiency implies that the information θt covers all the publicly available information at the moment t. 3. A strong form of market efficiency implies that the information θt covers all the information both publicly available and not publicly revealed at the moment t. It is worth noting that if the market is efficient in a weak form, there is no place there for above-average profits to be generated by technical analysis. In conclusion, it is worth quoting Samuelson: “there is no way of making an expected profit by extrapolating past changes in the future price, by chart or any other esoteric devices of magic or mathematics. The market quotation in t already contains in itself all that can be known about the future and in that sense has discounted future contingencies as much as it is humanly possible” (Samuelson 1965, p. 44). Later models of financial markets were generally kinder to technical analysis. In 1980, due to the famous publication by Grossman and Stiglitz (1976, 1980), the noisy rational expectations model gained a lot of popularity. These authors observed a characteristic paradox: if the market is efficient, investors have no incentive to collect and process new information, especially since it is associated with significant costs. In a nutshell, some investors may deliberately choose not to invest on the basis of exchange information because they would have to assign their own time and money; and if the market is efficient, they do not do it because there could be no advantage of doing it. As a result, when the market is in equilibrium, the socalled noise traders do not follow the information from the market. Summa summarum, the model by Grossman and Stiglitz, operates on the premise that market inefficiency would be increased as the cost of gathering and processing the information increases. Although the noisy rational expectations model, in its most original form, does not allow for technical analysis (Grossman and Stiglitz assume that uninformed investors have rational expectations about future prices), this gap has been filled by subsequent variations (Hellwig 1982; Brown and Jennings 1989). For example, Blume, Easley, and O’Hara (1994) come to the conclusion that trading volume may contain information about the “quality” of market actors that, combined with the observation of prices, can increase the effectiveness of technical analysis. Another type of model that view technical analysis more favorably are behavioral models. A typical behavioral model usually contains two

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categories of investors: rational arbitrageurs, who buy undervalued and sell overvalued instruments (the so-called sophisticated investors or smart money traders) and noise traders (Irwin and Park 2008). Noise traders rely upon market noise when taking their decisions; treating it as if it were valuable information. Behavioral models are based on two fundamental assumptions that favor technical analysis: on the one hand, the noise traders buy stocks when prices are rising and sell when prices are falling. Thus, they increase the demand or supply at those key moments and thus generate trends. On the other hand, rational investors, due to multiple limitations of arbitrage, are not always willing and able to cause prices to return to a fundamental equilibrium (Schleifer and Vishny 1997). When discussing behavioral models, it is also worth mentioning the book of Kahneman and Tversky of 1974. The two researchers from the University of Tel Aviv point out in their book that an anchoring effect may exist in the market, according to which the investors are too strongly attached to current market prices. As a result, the flow of new information the market causes prices to react not immediately, but gradually, as investors are too strongly “anchored” to historical rates. Also, Kahneman and Tversky (1974) and Shefrin and Statman (1985) draw our attention to the possible emergence of trends as a result of the improper assimilation of information. They note that when the prices fall down, the investors are unwilling to sell instruments so as not to “cash out” on losses, but at the same time, when the prices increase, the stock players are willing to sell in order to materialize their gained profits. As a result, the supply on the market may not be sufficient to bring the prices to a new and lowered fair value after bad news, and supply will be too high after good news, thus inhibiting growth. What’s more, the investors may exhibit a cognitive conservatism that causes them to treat new information too cautiously at the beginning, thus rendering them unable to properly assess the opportunity to continue a good or a bad run. Consequently, both cases occur in conditions that replace an immediate jump in prices with a gradual change; that is, a trend. Finally, one should also pay attention to such phenomena as the confirmatory bias and representativeness heuristics, which may cause investors to erroneously expect increases in the near future that would be similar to what had just occurred in recent history (Wason 1960; Kahneman and Tversky 1974). Herd models (Froot, Scharfstein, and Stein 1992) suggest that the rational investors who recognize that the instrument is undervalued and decide to buy it can earn money on the transaction only, if there are also other investors who generate sufficient demand to move the price back to the equilibrium. The resulting effects of the herd may be so strong that some investors may try to make money by analyzing only the behavior of prices, which, after all, are not directly related to the inherent value of the company. In addition, by acting in herds, the technical investors may themselves “push” prices in the expected direction and transform their expectations into a self-fulfilling prophecy. The effects of the herd are also referred to by de Long, Shleifer, Summers, and Waldmann (1990) as well as Bikhchandani,

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Hirshleifer and Welch (1992), who note that when prices rise, some investors may wish to “join the train before it escapes.” Such behavior is also observed among participants in investment funds, who tend to transfer their assets from funds that do not meet their expectations to the ones performing better. The next set of models is associated with the market microstructure; examples include order flows (Osler 2000). The stop-loss and take-profit indicators tend to focus in the vicinity of the tops and bottoms and round numbers. Thus their simultaneous activation may result in a significant increase or decrease in prices, which may be a justification for the operation of, for example, short-term breakout systems. Some authors also point out that various institutions may be responsible for trend formation. For example, Silber (1994) partly blames the central banks that, by focusing on their own goals, may sometimes hinder the immediate and full discounting of the fundamental information on the currency exchange markets, and thus lead to the formation of trends. One the other hand, Garleanu and Pedersen (2007) see one of the causes of trend formation in contemporary risk management methods. VaR, for example, is generally calculated using track records, therefore, if there is a sharp decline in the market prices of selected instruments, the financial institution with a specific risk budget may be forced to sell part of the analyzed assets. As a result, prices may fall even more and give rise to a trend in the market. Many other concepts exist in the market; nonetheless, they have not gained such wide popularity as the concepts described above. We could talk about, for example, chaos theory (Clyde and Osler 1997) or about the hypotheses about risk premiums for technical analysts (Irwin and Park 2008, p. 940). Finally, some authors believe that the effectiveness of technical analysis may be due to the periodic market inefficiencies; but, as they are discovered and used by market players, they gradually disappear (Marquering, Nisser, and Valla 2006). * *

*

The current concepts in existing literature are aimed at explaining the merits of developing positive returns using technical analysis in different ways. To do so, they refer to various mechanisms involved in many aspects of the operation of the financial markets. For this study, it is important that the literature presents several theoretical grounds that justify the profitability of technical analysis.

Managed Futures Benchmarks In this and the next chapter, I will make a review of benchmarks for the managed futures industry, as well as review potential biases and data distortions.

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Currently quite a wide range of different types of indices for CTAs and managed futures exist in the market. In classic stock markets there are both the indices that directly track the behavior of instruments (S&P500, WIG20) and the investment fund indices (Morningstar, Lipper Tass). The situation in the market of CTAs is quite similar. In this case, the individual indices can vary considerably between each other in terms of selection of managers and instruments for the portfolio, weighting system, presentation of data, periods, index composition adjustment, etc. As a result, although all the indices aim to reflect the same industry, due to the differences in the technical details of their composition, the distribution of return rates of individual indices may significantly differ from each other (Schneeweis and Spurgin 1997). Nonetheless, there is a noticeable tendency among the CTA indices to meet the criteria listed below, which are a standard in stock and bond markets. We are talking about the five characteristics that should be appropriate for financial indices. These are: 1. Unambiguity: The information on the composition of the index and its components should be clearly predefined, transparent, and accessible to consumers. This applies, for example, to the CTAs included in the index, their weights, factors or strategies that the index is tracking, and rules for verifying and rebalancing the index. 2. Investability: Although it is not required that investors are able to invest in the indices or their components directly, it is expected that investors have an opportunity to achieve rates of return represented by a given index at minimum cost and tracking error. 3. Measurability: The investors should have access to prices and rates of return of the instruments included in the index so as to make it possible to verify the correctness of its quotations. 4. Appropriateness: The index should take into account the investments that are available to the investor and exclude those that are unreachable. The same applies to rebalancing and weighting system, which should be designed to reflect the point of view of potential users. 5. Accountability: Changes in the composition and rules of construction of the index should be made by a transparent and trustworthy committee and all decisions should be based on explicit and predetermined rules.

Classification of Managed Futures Indices The broadest division of the CTA indices exists in the section of investments that may be included in the index portfolio (see figure 3.14). We can distinguish two broad categories (Schneeweis and Spurgin 1997). The first type is the manager-based indices, also called active indices. The second category is the security-based indices, also called passive indices. This term refers to the indices representing simulations of specific futures investment strategies.

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Managed futures indices

Active indices

Investable indices

Figure 3.14

Passive indices

Non-investable indices

Classification of managed futures indices.

Source: Author’s elaboration.

Active indices Within the subcategories of the active indices, there is a further breakdown into two types: non-investable indices and investable indices. Indices from the category of non-investable ones, are usually created by companies that maintain the CTA database and include the accounts and the funds entered into the database. It is here that they can serve both as funds open to new investments and closed funds, which makes the rate of return on the entire index difficult to be achieved by an average investor. In addition, it should be noted that the CTAs do not have the obligation to report to databases and they are allowed the discretion regarding such decisions regarding whether they should report to any databases, and if yes, to which databases they may provide their results. As a result, no base, and hence no index, represents the full universe of the CTA, so their quotations may vary significantly from each other. Unlike non-investable indices, investable indices are usually based on a smaller group of funds that report directly to the index provider and for which the prerequisite is that they are available to new investors. The fundamental difference between investable and non-investable indices is that the companies that calculate them give their customers the opportunity to invest in the entire index, which is a sample of the universe of the given CTA. For both investable and non-investable indices, the quotations for the products offered by individual providers may vary significantly from each other. This is a consequence of the different rules of construction that involve a number of key issues. Criteria and Selection Process The selection criteria concern the rules that determine whether a manager is to be included in the index or not. These may be quantitative and qualitative criteria. Index providers usually select the components in such a way that a specific sector or exposure to market factors is reflected. Most quantitative criteria relate to the minimum size of AUM and a minimum length of the track record.

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Classification by Style Different index providers may apply not only different divisions of CTA investment management styles, but also different criteria that guide the decision of whether to qualify or reject the manager of the sub-index representing the specified style. What’s more, there may also be differences in the calculation of indices covering the full universe of the CTA, in which the classes are weighted in different ways. Number of Investments in the Portfolio Among index providers, there is no consensus as to how many managers should be included in the index. The actual number may vary, primarily in terms of stringency of the criteria adopted by the institution that publishes the index. In fact, it is unnecessary bring in many CTAs, as studies show that even a portfolio of 4–6 different managers provides sufficient diversification and allows the index to represent a sector or a strategy (Schneeweis, Gupta, and Remillard 2008). The number of investments needed to achieve the required level of diversification may, however, vary, depending on the nature of the weighting system and management styles of each CTA. If the index is asset weighted, then it may happen that it may be representative of only a small group of CTAs having the largest AUM. On the other hand, if some CTAs are characterized by much more volatile results, then they may dominate the behavior of the entire index. Method of Weighting Method of weighting refers to the weight assigned to each CTA fund comprising the index. The two most popular methods are the asset-weighted indices and equal-weighted indices. Both methods have their advantages and disadvantages. The asset-weighted indices are characterized by the dominance of large funds. In other words, they are formulated under the assumption that the CTA having large AUM represents the industry better than a smaller fund, which is not always true. A recipe for this problem could be equal-weighted indices, but they too possess certain drawbacks. In this case, the small funds, which may not necessarily be open and accessible to many investors, are over-represented. Subindices Most CTA index providers publish not only a single global index, but also provide many subindices. In terms of subindices the funds are usually divided by two criteria. On the one hand, there is a division based on the type of investment strategy (systematic and discretionary strategies); on the other hand, a segmentation based on the markets that the CTAs operate on, such as the currency market, the stock market, etc. is also distinguished. (Classification and definitions by Schneeweis, Gupta, and Remillard 2008).

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Types of Investment Strategies Discretionary strategies—the CTAs operate in the markets of currency, commodity, and financially variable futures and options, and make their decisions based on macroeconomic data and their own beliefs. Systematic strategies (or systematic trading)—the CTAs make their decisions using automated transactional systems, mainly based on technical analyses. Most of these systems are trend following ones, although there is also a segment based on countertrend or reversion systems. The group of funds being discussed may focus on long-, medium-, or short-term trends. Sectoral Indices Currency sector—funds operate on the market of currency options and currency futures and forwards. Diversified portfolio—funds operate in the markets of financial and commodity options and futures contracts, as well as of currency options and currency futures and forward. Financial sector—funds operate in the markets of financial option contracts and futures as well as of currency futures and forward contracts and currency options. Commodity sector—funds operate in the markets (OTC) of listed and unlisted futures contracts and options on energy raw materials, agricultural products, industrial metals, and ores. Stock sector—funds operate in the markets (OTC) of listed and unlisted futures contracts and options on instruments connected with the stock market. Passive Indices Passive indices are not based on the results of CTAs and managed futures funds, but they derive their value directly from trading in individual instruments. These indices are designed to simulate the results of specific investment strategies in the futures market and thus reflect the results of a specific CTA market segment. Due to the fact that the purpose of the passive indices is to reflect the performance of the investment strategies of CTAs, they are usually based on trend following systematic strategies because, by assumption, it would be impossible to construct indices based on discretionary strategies. A comprehensive overview and discussion of passive indices can be found in Spurgin (1999a).

Overview of the Selected Indices Among the three described groups of indices, passive, active investable, and active non-investable, by far the largest group are the last ones.4

●●

Examples of Active Non-investable Indices CASAM/CISDM. The CASAM/CISDM indices are calculated using the CASAM/CISDM Hedge Fund/CTA Database. The indices are calculated

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●●

●●

83

and published every month, and even though this institution itself has calculated them since 1992, the indices have also been calculated back to 1979. In addition to equal-weighted and asset-weighted global indices, CASAM/CISDM also calculates the subindices for the currency, financial, and diversified segments as well as for systematic and discretionary strategies. The advantage of CASAM/CISDM indices is primarily a long history and monitoring of the broad investment universe. Barclay Group. Equal-weighted indices by the Barclay Group include many types of financial markets and investment strategies. Averages are calculated on the basis of the Barclay Group CTA Database, and have been calculated back to 1979 with per month values. In addition to the subindices, Barclay also publishes two global indices, representative of the entire universe of managed futures/CTA funds. The advantage of Barclay indices lies primarily in a long history unfortunately distorted, in its initial part, (supplemented back) by a range of biases (this subject will be further described later in this book). Credit Suisse Managed Futures Liquid Index (formerly Credit Suisse/ Tremont Managed Futures Index). It is an asset-weighted index for which, unlike in case of Barclay or CISDM, no subindices are computed. Portfolios included in the index rely upon the TASS databases. This index is published every month with the values calculated back to 1994.

Examples of Active Investable Iindices Active investable indices are a younger and more modest family of indices of the CTA/managed futures industry. Among the most recognizable indices there are the ones calculated by Credit Suisse/Tremont and S&P, although there are also the indices computed by MSCI and FTSE in the market. The major advantage of the investable indices is that they allow the investors to allocate money in a rather simple way. They provide an affordable opportunity to gain exposure to the managed futures industry. ●●

●●

Dow Jones Credit Suisse Blue Chip Managed Futures Hedge Fund Index (formerly: Credit Suisse/Tremont Managed Futures INVX Index). This is an asset-weighted index constructed and based on CTAs selected from the Tass database. The Credit Suisse/Tremont Managed Futures INVX Index was established in 2004 and is verified and rebalanced every six months. Index entry restrictions concern the minimum size of assets amounting to 50 million USD and a track record of not less than twelve months. S&P Managed Futures Index. The equal-weighted S&P index was created in 2002 in order to enable the investors to gain exposure to the managed futures industry. It is not offered as an investment product any more. The aim of the index was to track the systematic segment of the managed futures; especially products based on automated transactional systems and technical analysis.

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84 ●●

●●

●●

Newedge CTA Index (formerly Calyon Financial Index). Newedge offers its customers equal-weighted indices representing a broad range of managed futures funds. A fund must meet a number of conditions in order to be included in the index. First, a minimum level of assets is required which, in 2010, amounted to 800 thousand US. dollars. Second, the fund must be open to new investments. Third, it must publish the results every day. The Newedge index is composed of a minimum of twenty portfolios managed by different managers and is reconstructed every year. Lyxor CTA Long Term Index and Lyxor CTA Short Term Index. Lyxor provides the investors with many hedge fund and managed futures indices that share the similar characteristics. They are selected from the funds available on the Lyxor investment platform, on which the minimum level of AUM is 3 million USD. All Lyxor indices are asset-weighted and rebalanced every month. Valuations are published daily, but the liquidity of purchase and sale of units has been limited to weeks. Barclay BTOP50 Index. The aim of another index from the Barclay family is to reproduce all the results of the CTA industry with all styles of management and market segments. The funds included in the portfolio are selected following the top-down philosophy, and they are the largest CTA funds in terms of AUM. Selected funds must represent not less than 50 percent of the industry in terms of assets. The index has been calculated on a monthly basis since 2003.

It is worth mentioning that not all companies that calculate the managed futures indices publish their composition, calculation methods, etc. Some providers do not publicly disclose virtually any information beyond the quotations themselves. Alpinvest from Amsterdam, for example, operates this way. Examples of Passive Indices Only a few passive indices currently exist in the market; the Mount Lucas Management Index (MLM Index) is the most popular among them. The MLM index is composed of twenty-two futures contracts, eleven commodity contracts, six currency contracts, and five fixed-income or money market contracts. The contribution of each basket is weighted in the total index of the portfolio, relative to historical volatility. Then, individual markets within each basket are balanced. Positions in futures contracts are long or short; the design of the index does not assume neutral positions. Whether a long or a short position is taken, depends on whether the last closing price was higher or lower than the twelve-month long-term average. Due to the high correlation with the CTA results, the MLM Index is widely used as a benchmark for managed futures, although it should be noted that a tracking error for individual funds may be quite large (Schneeweis and Spurgin 1997). Proposals of other passive indices are described, for example, by Lequeux and Acar (1998), Waksman (2000), Jaeger, Cittadini, and Jacquemai (2002), as well as by Pojarliev and Levich (2008). Table 3.5 contains a summary of the CTA indices available on the market.

Value weighting Equal weighting Value weighting Weighting method

BarclayHedge trader index

Credit Suisse managed futures liquid index

Index

Value weighting Many criteria Value weighting Value weighting Equal weighting Weighting method

Dow Jones Credit Suisse Blue Chip managed futures hedge fund index

FTSE CTA/ managed futures

Lyxor CTA long-term index

Lyxor CTA short-term index

Newedge CTA Index

Index

Many criteria Equal weighting

MSFB

AFX currency management

–

December 31, 1979

December 31, 1979

December 31, 1979

Backfilled since

January 3, 2000

December 11, 2007

December 11, 2007

September 1, 2004

April 18, 2005

December 31, 1986

1998

2003

1988 January 1, 1984

Exist since Backfilled since

2000

2007

2007

2004

2003

2003

Exist since Backfilled since

2003

1987

1992

2001

Exist since

–

3

3

Subindices

–

–

–

–

–

–

Subindices

–

6

7

7

Strategies

Miesięczna

Dzienna

Monthly

Publication frequency

Daily

Daily

Daily

Daily

Monthly

Monthly

Publication frequency

Monthly

Monthly

Monthly

Monthly

Publication frequency

–

–

–

Constituency disclosure

Yes

Yes

Yes

Yes

Yes

Yes

Constituency disclosure

No

No

No

No

Constituency disclosure

Source: Author’s elaboration based on https://www.isenberg.umass.edu/CISDM/Hedge_FundCTA_Indices; http://www.barclayhedge.com; http://alternativebeta.credit-suisse.com; http://www. lyxor.com; http://www.newedge.com; https://www.mtlucas.com; http://people.stern.nyu.edu/rlevich/afx_index.html; Bloomberg; Schneeweis, Gupta, and Kazemi (2008), p. 273.

Many criteria

MLM

Passive indices

Equal weighting

BTOP50

Active investable indices

Equal weighting

CASAM/CISDM CTA asset weighted index

Weighting method

CASAM/CISDM CTA equal weighted index

Active non-investable indices

Index

Table 3.5 Characteristics of the managed futures indices

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Performance of Managed Futures Indices Due to the significant diversity in the design of the managed futures indices, they historically offered different rates of return. Figure 3.15 shows the behavior of the CTA indices between 1999 and 2010. Many of the indices are strongly correlated with each other, however the correlation with conventional asset classes is low. Table 3.6 shows correlation coefficients between individual managed futures indices and traditional asset indices. Correlations are typically very high and come to 0.8–0.9. An exception to this is the MLM index with its correlation with other managed futures indices of about 0.5; although it is interesting to note that it is the only passive index in the analyzed group. Despite the strong correlation, the indices existing in the market show many differences in the average historical return rates and levels of volatility. This is illustrated in table 3.7. The highest averaged return rates over the past decade were recorded by the Credit Suisse Managed Futures Liquid Index, although it showed a large degree of volatility. On the other hand, the MLM index showed the lowest rate of return and the smallest volatility. This effect can be explained by the lack of leverage, which is quite common in typical futures contracts funds. It is worth noting that all the indices generated higher average annual returns at much lower risks than the domestic stock market. Obviously, this also translates to a higher modified Sharpe ratio.

450 400 350

CASAM/CISDM CTA Asset Weighted Index CASAM/CISDM CTA Equal Weighted Index Barclay Trader Indexes CTA Barclays US Managed Futures Industry BTOP 50 MLM Index Monthly Total Return Eurekahedge CTA / Managed Futures Hedge Fund Index Dow Jones Credit Suisse Managed Futures Hedge Fund Index Credit Suisse Managed Futures Liquid Index

300 250 200 150 100 50 0 1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 3.15 Managed futures indices from December 1999 to October 2010. Source: Author’s calculations.

0.94 0.92 0.94 0.53 0.91 0.96 0.81

Barclay Trader Indexes CTA

Barclays US managed futures industry BTOP 50

MLM index monthly total return

Eurekahedge CTA / managed futures hedge fund index

Dow Jones Credit Suisse managed futures hedge fund index

Credit Suisse managed futures liquid index

Source: Author’s calculations

1.00

CASAM/CISDM CTA Equal-Weighted Index

CASAM/CISDM CTA Asset Weighted Index

0.80

0.92

0.97

0.50

0.92

0.98

1.00

0.94

CASAM/ CISDM CTA Equal-Weighted Index

0.79

0.91

0.98

0.47

0.93

1.00

0.98

0.92

Barclay Trader Indices CTA

0.78

0.94

0.93

0.50

1.00

0.93

0.92

0.94

0.65

0.52

0.47

1.00

0.50

0.47

0.50

0.53

Barclays MLM US managed Index futures monthly industry total BTOP 50 return

Correlations between managed futures indices from December 1999 to October 2010

CASAM/CISDM CTA Asset Weighted Index

Table 3.6

0.78

0.90

1.00

0.47

0.93

0.98

0.97

0.91

Eurekahedge CTA / managed futures hedge fund index

0.82

1.00

0.90

0.52

0.94

0.91

0.92

0.96

1.00

0.82

0.78

0.65

0.78

0.79

0.80

0.81

Dow Jones Credit Credit Suisse Suisse managed managed futures hedge futures fund index liquid index

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Table 3.7 Rate of return on managed futures indices from December 1999 to October 2010 Average annual rate of return (%)

Standard deviation (%)

Modified Sharpe ratio I

CASAM/CISDM CTA Asset Weighted Index

7.7

5.8

1.33

CASAM/CISDM CTA Equal-Weighted Index

8.8

6.6

1.34

Barclay Trader Indexes CTA

5.9

5.3

1.12

Barclays US managed futures industry BTOP 50

6.3

7.1

0.89

MLM index monthly total return Eurekahedge CTA / managed futures hedge fund index Dow Jones Credit Suisse managed futures hedge fund index Credit Suisse managed futures liquid index

4.1

5.1

0.80

12.5

7.0

1.79

7.3

8.2

0.89

13.9

10.0

1.39

Note: Sharpe ratio, assuming rf = 0. Source: Author’s elaboration based on data from Bloomberg.

Bias of Databases and Indices of Futures Funds This chapter describes the potential biases of managed futures, which may distort the characteristics of the rates of return they represent. Financial market indices do not reveal the whole truth to the investor. While their purpose is to reflect the characteristics of each asset class, market segment, etc. most accurately, they are not free from all sorts of biases that distort the actual picture of the data being presented. The issue of biases in the market of stock and financial indices is widely known in the literature. This problem affects not only the conventional index funds (Brown, Goetzmann, Ibbotson, and Ross 1992), but also managed futures indices (Bhardwaj, Gorton, and Rouwenhorst 2014). The literature lists several categories of index biases that have an impact on the perception of both the expected return and the volatility.

Selection Bias Selection bias or self-selection bias results from the voluntary act of reporting the investment performance to databases. The managers who did not record satisfactory rates of return may be reluctant to publish their achievements (Solnik and McLeavey 2009, p. 214). On the other hand, the managers, who were successful, may report their achievements to several databases

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or give up reporting at all if they wish to open a new fund to attract new customers, which would mean that they no longer treat the results of the “old” fund as “marketing material” (Jaffarian 2009, p. 195). An attempt to estimate the impact of the selection bias on the rates of return in the databases was made by Park (1995). In order to do so, he applied the switching regression models. According to the estimates by Park, in order to eliminate the effect of the selection bias, the average rate of returns of the CTA should be adjusted down by about 2.1 percentage points (Park, Brown, and Goetzmann 1999). It is worth noting that the broadly meant selection bias can take on many forms. For example, in case of asset-weighted benchmarks, the indices are more influenced by large funds whereas in the case of the equal-weighted benchmarks, the indices are offset by the impact of higher volatility funds (Schneeweis, Gupta, and Remillard 2008). What’s more, the effect of selection associated with voluntary reporting does not affect the indices to a greater degree, causing its developers to now require dozens of months of track records and not allowing for re-reporting funds that had already resigned from this.

Look-back Bias Look-back bias is another bias resulting from the voluntary nature of reporting the investment performance to databases (Jaffarian 2009, p. 195). It is connected with the fact that the manager may at any time discontinue reporting the results or start reporting them again. For example, if the CTA generates poor investment results, the manager may decide not to publish them, and when they improve, the manager may return to the earlier practices. As a result, the process of selective reporting may lead to excessive average rates of return in the database. Unfortunately, the extent of the impact of the look-back bias is difficult to estimate because, in practice, it requires information that has not been submitted to the database and, by definition, is not available (Bhardwaj, Gorton, and Rouwenhorst 2014). At this point, it is worth noting that this problem primarily concerns the databases rather than the indices; in the latter case, there is usually no practice to revise their track records.

New-Manager Bias The new managers of managed futures usually have fewer AUM and control more concentrated portfolios. As a consequence, their results do not reflect the mature large funds, which might cause a systematic data bias effect called the new-manager bias (Schneeweis, Gupta, and Remillard 2008, p. 277). To avoid this distortion, the index providers usually ignore the first twelve to twenty-four months of the reported track record or establish the

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requirement of a minimum level of AUM (Schneeweis, Gupta, and Remillard 2007). In the existing literature, it is difficult to find a more comprehensive study analyzing the scale of the impact of the new-manager bias on the data concerning the managed futures market.

Backfill Bias The backfill bias in existing literature is sometimes referred to as the instanthistory bias (Bhardwaj, Gorton, and Rouwenhorst 2014). When the asset manager decides to publish his results in a database for the first time, he also provides information about the historical rates of return of his fund. It is obvious that he wants to show his achievements in the most favorable light, so there may be a tendency to show the track record from a period that seems particularly attractive. Such a situation can forge a misleading picture of the whole industry, put pressure on the inflated rates of return, and run the risk of undercutting (Schneeweis, Gupta, and Remillard 2008, p. 277). The phenomenon of backfill bias is fairly well described in the literature discussing the hedge fund sector, where the methodology of studying the phenomenon in the CTA area originates. Barry (2003) analyzed the TASS hedge funds database and made an estimation of how far back a track record the funds provide when they start publishing their results. According to the Barry’s estimates, 80 percent of the CTAs provide historical data for at least six months, 65 percent for at least one year, and 50 percent for at least two years. What’s more, it turns out that the funds, when reporting their data to several databases, rarely provide historical information for the same period. Liang (2000) has analyzed 456 funds that published their results in the HFR and TASS databases, and noted that only 154 of them (33 percent) had the same date of first published pricing (rate of return). The attempts to estimate the backfill bias are usually based on a study of the average rates of return for x (any given) number of first months of listing the index in the database. The first such study was conducted by Park (1995). For hedge funds, the analyses estimate that the average annual level of backfill bias is 1.2–1.4 percent (Lhabitant 2004). For example, Fung and Hsieh, in their analysis conducted in the years 2000 and 2001 on the basis of the TASS database, have estimated its annual value for the years 1994–1998 to be 1.4 percent, while Edwards and Caglayan, while analyzing the MAR database for the years 1991–1998, have achieved the value of 1.2 percent. More recent studies can be found, among others, in Posthuma and Van Der Sluis (2003), Malkiel and Saha (2005), and Ter Horst and Verbeek (2007). The studies on the backfill bias for managed futures have a shorter history, but they are rich enough to prove the significant impact of this bias on the data. One of the most comprehensive analyses was conducted by Bhardwaj, Gorton, and Rouwenhorst in 2014. The study was conducted on CTA quotations in the Lipper Tass database, which has the advantage that it is possible to say which data was uploaded as historical and which

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as current.5 The first and the most important conclusion from their research is that the backfill bias plays (Bhardwaj, Gorton, and Rouwenhorst, 2014, p. 11, Table 2) a significant role in the distortion of the information contained in the databases. It should also be noted that this bias would most likely not be eliminated merely by removing the first x number of months from the record. A CTA index not free from historical data presented the rates of returns as only 1.1 percent lower than the index did after the elimination of the data for the first 12 months. In turn, after the elimination of all historical data prior to the first reporting of the fund to the database, the rate of return shrunk by as much as 4.5 percent. This could mean that, in the database, there was a large group of funds that provided a very long track record and extremely high rates of return, which over time proved to be unrepeatable. Concluding the discussion on the backfill bias, it is worth mentioning that today, database providers try to, at least partially, reduce it by, for example, indicating the initial date of reporting (see above). Moreover, the backfill bias has little impact on the indices because they are designed to avoid this effect. In general, the manager of the managed futures funds and CTAs usually cannot provide their historical performance to the indices because their values are not retrospectively adjusted. This problem affects only the initial history of older indices that were constructed based on the databases.

Survivorship Bias The effect of survival (called survivorship bias) is probably the most well documented bias in the literature on biases of the CTA and managed futures database. Survivorship bias occurs when the fund quotations are excluded from the index or database, after the fund ceases trading. Very often, it occurs on the initiative of the managers and investment companies themselves, which, after ceasing their services to the investors, ask the database providers to remove their historical performance (Capocci 2004, p. 49). As a result, the investor analyzing a database inevitably focuses only on those funds that have stood the test of time. It is easy to deduce that if some funds have managed to survive in the industry, it is quite likely that their investment results are better than the results of others. The consequence might be exaggerated average rates of return contained in the database, compared to the actual results of the managed futures asset classes. The phenomenon of survivorship bias has a big impact on many types of investment funds, both hedge and classic. Nonetheless, this bias can be much larger in the case of managed futures funds. We make such a supposition in view of the fact that in this industry, we deal with a relatively high mortality rate6 of funds over a short period, as evidenced by many studies. Brown, Goetzman and Park (2001) estimated the average life span of a CTA (median) as 24 months, and that up to 20 percent of funds are liquidated annually. Similar conclusions were drawn by Diz (1996), who analyzed a sample of

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CTAs in the years 1989–1995 and found that almost half the funds comprising the sample had “died” in that period. Also, Spurgin (1999b) has noted a similar attrition rate. The sample analyzed by him in the years 1994–1995 showed that 22 percent of the funds died each year, whereas small CTA funds were characterized by the highest rate of attrition. In turn, according to statistics presented by Fung and Hsieh (1997a), the average annual attrition rate between 1989 and 1995 was 19 percent. In addition to fund mortality, a meaningful picture of the industry is presented by the statistics on the average “lifetime” of CTAs. Gregoriou, Hubner, Papageorgiou, and Rouah (2005), for example, after having analyzed the Barclays database from the years 1990–2003, estimated the average lifetime of the CTAs to be just 4.2 years. This result was a consequence of a very high attrition rate among small CTAs. In addition, these four researchers have noted that the funds that are alive longer are usually characterized by better performance and lower leverage, which allows them to better cope with liquidity management. One of the more recent and extensive studies on the CTA lifespan was presented in 2008 in a paper by Gregoriou and Rouah. These researchers have analyzed 546 active, and 965 defunct, CTAs that had reported their performance to the database of the Barclays Group. The period of analysis covered 216 months (from January 1988 to December 2005). Their results have revealed that the average lifetime of a CTA was 4.7 years, and it was significantly longer for larger, rather than smaller, funds. The funds with AUM below 10 million USD survived 4.3 years on average, while those with AUM higher than 10 million USD survived 6.8 years on average. Some databases store the segregated statistics for both existing and dead funds. A comparison of the distribution of rates of return of both samples allows us to estimate the value of the survivorship bias. The value of this effect is defined as the difference between the average returns of the funds that have survived, and the rate of return of all funds (Fung and Hsieh 1997a, p. 6). Malkiel (1995) applied this method to evaluate classic investment funds and hedge funds; as did Ackerman, McEnally, and Ravenscraft (1999); Brown, Goetzmann, and Ibbotson (1999); Fung and Hsieh (2000); and Liang (2000). Interestingly, in the case of hedge funds, very large discrepancies were found: from 20 base points (Ackerman, McEnally, and Ravenscraft 1999), up to as much as 840 base points (Malkiel and Saha 2005). These differences might be the result of, for example, the structure of the databases, which often have only a limited percentage of the same funds. It is interesting that similar biases exist for CTAs and managed futures. Fung and Hsieh, in the study of 1997, found in years 1989–1995, an average monthly return of 1.61 percent for operating funds and 0.81 percent for funds that have ceased to exist, and Chance and Billingsley (1996) achieved similar results in almost the same period. The survivorship bias itself, calculated according to the above definition, has been estimated to 9 percent on an annual basis by Diz (1996), and 3.5 percent by Fung and Hsieh (1997a).

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Several studies on the survivorship bias concerning the CTAs deserve a moment of deeper reflection. Gregoriou and Rouah (2008), in their aforementioned analysis of the Barclay Group database, estimated the discussed effect while making a division into different size-classes. They found that the survivorship bias is higher for smaller CTAs. In this particular group, the difference between the rates of return on the existing CTAs and all CTAs was the highest; while in the group with the largest volume of assets, the difference sometimes was even in the negative. When aggregating the full sample, the scale of the phenomenon was minor and came to 9 base points. An interesting experiment was also carried out by Capocci (2004), who calculated the survivorship bias in different five-year subperiods for the years 1985–2003. According to calculations by Capocci, the bias varied from 4.4 percent between 2000 and 2003 to 7.3 percent between 1990 and 1994. When concluding our review of the existing literature on the subject, we cannot forget about the already mentioned study by Bhardwaj, Gorton, and Rouwenhorst (2014). This study is interesting not only because it is based on relatively new data from the Lipper-TASS database (years 1994–2007), but also because the authors have estimated the combined impact of these two effects: backfill bias and survivorship bias (Bhardwaj, Gorton, and Rouwenhorst 2014, p. 8.) According to these authors, the value-weighted portfolio of CTAs, the statistics of which had been calculated before taking into account the impact of both these phenomena, brought an average annual rate of return of 12.6 percent with variability of 11.8 percent. This implied a Sharpe ratio of 0.73 percent. After lowering the results with the survivorship bias and backfill bias, the rate of return had fallen to just 4.9 percent. In conjunction with a variability of 9.7 percent, it has translated into a Sharpe ratio of 0.09. This means that after taking into account the impact of defunct funds and historical quotations made available to the databases, the CTA funds generated only a minimum risk premium. Although previous studies have shown that the survivorship bias is one of the biases that significantly distort the performance of the managed futures industry, nevertheless, some authors raise the point that its impact on the CTA indices is quite limited (Schneeweis, Gupta, and Remillard 2008). This is because the index providers usually do not adjust their historical values by excluding withdrawing funds. It should be noted that some indices bear the criterion of a minimum volume of assets. Thus the CTAs that have ceased to operate due to the non-satisfactory rates of return and decreased value of the assets under their management are not taken into consideration when calculating the index in the last phase of activity. It is de facto assumed that the investors withdrew their money from the fund some time before it is dissolved. This assumption is, unfortunately, not realistic, and may place positive pressure on and artificially inflate the rate of return on the index. Similar to the biases mentioned earlier, the survivorship bias can be eliminated by using an index that does not revise its data. Moreover, from the investor’s point of view, such indices can bring a better picture of income,

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especially in the case of the asset-weighted indices whose share in the portfolio shrinks as the returns worsen. Three other interesting data biases have been stressed by Schwager (1996, pp. 67–81). They are interesting because, although they do not distort the results of the whole industry, they distort the picture of the investment parameters that are visible from the investor’s perspective.

Volatility Underestimation According to Schwager (1996), all indices mislead investors, in the sense that the investors usually underestimate the risk of potential investments and thus overstate the risk-return ratios (the so-called volatility-underestimation bias). The investors active in the managed futures market are unable to replicate index performance by transferring money in all funds included in the system. This is because the minimum amount of investment in the CTA is usually very high (at least 500,000 US dollars [Schwager 1996, p. 67]), so if we wanted to make investments in accordance with the structure of the index, we would probably need to have a very large amount of capital. This problem does not concern, for example, investors in the stock market, who are able to build a balanced portfolio according to an index that involves relatively small amounts of cash. Different managed futures and CTA funds usually have a relatively low correlation. As a result, a portfolio made up of multiple CTAs has a significantly lower volatility than its individual components. In effect, the performance of the index observed by the investor is characterized by lower market risk than it is typically able to achieve. Unfortunately, this effect affects all the indices, and adapting them to reflect the financial capabilities of the investor requires a mathematical modification of the time series. It should be noted here that the empirical research in the following section of this book are performed from the perspective of the investor who has sufficient money to build a diversified portfolio of managed futures, which means the volatility-underestimation bias will not play a significant role.

Rebalancing Bias The indices that use the arithmetic average of the rates of return of the CTAs comprising them (equal-weighted indices) in the calculation process, de facto assume not only equal volume of investments in all funds, but also a monthly rebalancing (the so-called implicit rebalancing assumption).7 This is because the investor must sell the stocks whose values have recently increased and buy the stocks whose values have recently decreased; that is, he must rebalance his portfolio in order to have a portfolio consisting of stocks of equal value every month. Moreover, even if the index is asset- or

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liquidity-weighted, its design contains an implicit rebalancing assumption, as long as the weights of individual funds are adjusted less frequently than once a month. In this case, the investor is simply rebalancing his portfolio, not based on equal weights, but based on another, predefined structure. The assumption of monthly rebalancing is unrealistic from the investor’s perspective. On the one hand, some funds are too small to allow it, while on the other hand, typical liquidity in the industry usually does not allow for a monthly withdrawal of any amount, leave alone transferring it to another CTA. In consequence of the implicit rebalancing, the investors may rely on an unrealistic, inflated investment performance, especially when the rates of return are characterized by a negative serial correlation. To better illustrate this distortion below, we present an extreme example. Let us suppose that there are two funds: CTA A and CTA B. On their bases, an equal-weighted index is calculated. In the first month, CTA A generated a rate of return of 50 percent, whereas in the next month, it lost 40 percent. The cumulative rate of return was negative and amounted to −10 percent. CTA B earned the same rate of return, but in reverse order: in the first month, it lost 40 percent, whereas in the next month it earned 50 percent. The cumulative rate of return was the same as for fund A and amounted to −10 percent. Now let us look at the results of the equalweighted index. In the first month, the rate of return on the index was an average of 50 percent and –40 percent, which amounts to 5 percent. In the next month, the situation remained the same and again, the average was 5 percent. Given the two months, the cumulative rate of return on the index was 10.25 percent. Eventually, although the two funds included in the index generated a loss, the index itself showed a positive rate of return. In such a situation, it is difficult to say that it was a true reflection of either the industry or the investment opportunities (see table 3.8).

Closed Investment Bias In practice, the CTAs that are quite successful usually impose maximum limits, above which they do not accept new investments from investors. The reason is that the increase in assets beyond a certain level could result in a

Table 3.8

Rebalancing bias—computational example Month no. 1 (%)

Month no. 2 (%) Cumulative rate of return (%)

CTA A

50

−40

−10

CTA B

−40

50

−10

5

5

Equal-weighted index Source: Author’s elaboration.

10.25

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worse fund performance, for example, by hindering the execution of orders or by overexploiting the inefficiencies in the market. The CTAs that reach this point will then be closed to new investors. However, when calculating managed futures indices, whether or not the individual components are available to new investors is not usually taken into account. As a result, the rate of return represented by the index may be difficult to achieve for the average investor, because not all its components are fully investable. This phenomenon is called the closed investment bias. * *

*

Publicly available information about the performance of the managed futures funds are subject to many biases that cause the systematic distortion of data. A review of existing studies leads us to the conclusion that the scale of these biases is significant enough to reduce the attractiveness of investment in managed futures, and, in fact, to completely exclude its reasonableness. Nonetheless, the biases existing in the market concern, first of all, the databases and, to a lesser extent, the indices, which are actually affected only by risk underestimation, rebalancing bias, and closed investment bias. This is because the indices are created from the data supplied on an ongoing basis rather than from historical data. Nevertheless, the awareness that there are multiple information biases gives rise to attempts to eliminate or reduce them, in order to obtain a clear picture of the actual nature of the investment from the investor’s point of view. There are many adjustment methods. In case of the biases on the information contained in the databases, a solution may be an adjustment of expected returns so as to account for the biases. However, this method may be cumbersome because the results of tests of scale of the discussed effects are not equal, and are sometimes quite different. An example might be the survivorship bias, where the estimates range from 3.5 percent (Fung and Hsieh 1997a) to 9 percent (Diz 1996). Next, an easy-to-implement technique is to use of the indices that are not revised to impose a minimum required level of AUM, and a minimum operation period of the CTAs comprising them. Unfortunately, by using only the indices, we cannot eliminate the biases that are inextricably linked with their design: volatility underestimation, rebalancing bias, and closed investment bias. In the end, two additional steps are necessary in order to reduce them. First, the choice of the appropriate indices—using the appropriate methods of weighting (weighting of assets) and the appropriate selection of its elements—is important. Second, it is necessary to adopt the perspective of an investor who enjoys the ability to diversify a portfolio. In addition, a mathematical time series adjustment that modifies their volatility to a level determined by the investor’s financial capabilities is recommended. Table 3.9 contains a synthesis of the discussed information biases and the proposed measures for reducing or eliminating them.

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Table 3.9 Biases on the databases and CTA indices and the proposed measures for reducing or eliminating them Types of biases

Area of impact

How to eliminate or reduce the bias?

Selection bias

Databases and indices

1. Decrease the average returns in databases by about 2. 1 p.p. (Park, Brown, and Goetzmann 1999)

Look-back bias

Databases

1. Use indices which are not revised backward

New manager bias

Databases

1. Discard the first 12–24 months of NAV history of each fund in a database

2. Use indices which are not revised backward

2. Employ a minimum asset under management threshold 3. Use indices which are not revised backward Backfill bias

Databases

1. Discard the first 12–24 months of NAV history of each fund in a database 2. Decrease the average returns in databases by about 4.9 p.p. (Bhardwaj, Gorton, and Rouwenhorst 2014). 3. Use of indices that are not revised.

Survivorship bias

Databases and partly indices

1. Decrease the average returns in databases by about 3–9 p.p. (Fung and Hsieh 1997a, b, Diz 1996).

Volatilityunderestimation bias

Indices

1. Recalculate time-series with Schwager’s (1996) formula

Implicit Rebalancing Assumption

Indices

1. Use value-weighted indices with a minimum asset under management threshold and are frequently rebalanced

Closed Investment Bias

Indices

1. Use only fund that accept new investors

2. Use indices that are not revised backward and which track funds till the liquidatation

2. Use equal-weighted indices

Source: Author’s elaboration.

In summary, it is worth noting that the managed futures industry is aware of the frailties of the existing databases and indices. Therefore, all kinds of measures and regulations are introduced to increase the accountability of the existing CTA benchmarks. A good example is CASAM CISDM CTA Asset Weighted Index. Its design assumes that the components of assetweighted index can only be included if they fulfill certain criteria. For example, the index’s scope encompasses only the CTAs having a minimum of 500 thousand USD under their management and a 12-month track record, and their rates of return are taken into account until the end of the operation. It seems that after mathematical modification of the time series of the CASAM/CISDM CTA Asset Weighted Index to adapt the volatility to a level accessible to the investor, it could provide a good benchmark that reflects the experience of investments in managed futures.

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With regards to the observations made above, the empirical analyses in this study will be carried out based on ready-to-use indices and not by relying upon databases. Among the considered indices, only the CASAM/ CISDM CTA Asset Weighted Index has desirable characteristics, which are described above; it has a sufficient track record and, in addition, includes the largest number of CTA funds. This particular index is investigated later in the book.

Performance of Managed Futures in the Light of the Existing Research Legitimacy of investments in futures funds is one of the key issues that appear in the literature on the subject. Most of the research relates to the use of this type of investment in the investment portfolio, which is associated with low dependency between the managed futures and the conventional asset classes. The following review has been carried out in chronological order. One of the first studies on this subject was an article by John Lintner of 1983. The research focused on the profit-risk profile of the CTA-managed accounts, and the author concluded it, stating that “the combined portfolios of stocks (or stocks and bonds) after including judicious investments . . . in managed futures accounts (or funds) show substantially less risk at every possible level of expected return than portfolios of stocks (or stocks and bonds) alone.”8 This article has become a point of reference for future studies, which confirmed the presented results or objected them. Elton, Gruber, and Rentzler (1987, 1989, 1990) carried out three studies on the subject. The first study (Elton, Gruber, and Rentzler 1987), which is important, concerning the Public Commodity Pools, demonstrated that the investments in the managed futures are not reasonable both as a stand-alone investment and as part of a broader investment portfolio consisting of stocks and bonds. The second and third studies focused on public offerings (Elton, Gruber, and Rentzler 1989) and protection against inflation (Elton, Gruber, and Rentzler 1990); and also gave results unfavorable to the CPOs. Other research on the CPOs have not yielded conclusive results. Irwin and Brorsen (1985) came to the conclusion that the CPOs allow for increasing the effective limit of investments, whereas Irwin, Krukmeyer, and Zulauf (1993), Schneeweis, Savanayana, and McCarthy (1991), as well as Edwards and Park (1996) have not found any sign of a managed futures–added value for the investors, neither as a standalone investment, nor in the context of a portfolio of stocks and bonds. One of the key disadvantages associated with the investments in the CPOs are high investor’s costs. Thus the literature definitely prefers the investments in futures contracts funds directly via CTAs. McCarthy, Schneeweis, and Spurgin (1996) came to the conclusion that the balanced portfolio of CTAs adds value to the classical portfolio of stocks and bonds, by diversifying it.

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Schneeweis, Spurgin, and Potter (1997), in turn, noted that the allocation of a part of the portfolio to an asset-weighted CTA index allows the investor to increase the Sharpe ratio, and Edwards and Park (1996) believe that, thanks to the favorable risk-return profile, the CTAs withstand both in the context of stand-alone investment and as part of a portfolio of stocks and bonds. What’s more, Kat (2005) notes further that the managed futures funds also fulfill an important function in the portfolios of alternative investments, because they allow the investors to neutralize the negative skewness and kurtosis. Later studies contain the results largely favorable to the CTA industry. Amenc, Goltz, and Martellini (2005) focused on a relatively short period from 1997 to 2004, when the futures contract funds recorded higher rates of return than the stock and bond market, at the same time showing lower risk measured by standard deviation. In addition, including the futures contracts funds in a portfolio allows for shifting the curve. A study by Amenc, Goltz, and Martellini (2005) raise, however, significant methodological doubts in both the substantive layer (CTA investments are treated as synonymous with Global Macro class hedge funds) and the computational layer (no verification of statistical significance). Nonetheless, these authors conclude that the CTAs may be a valuable component of the design of the portfolios of institutional investors. Shore (2005) analyzed the results of balanced portfolios of stocks and bonds in the years 1990–2003, which were extended by 10 percent share of equity futures and hedge funds. The portfolios including futures contract funds were characterized by the lowest risk measured by standard deviation and the highest (least negative) skewness and kurtosis nearest to zero. They also showed the highest level of the so-called S-ratio, that is, the ratio of the number of profitable to unprofitable months (Shore 2005). Later Shore (2008) repeated the analysis for the period extended to 2006, expanded the area of interest by passive investments in the commodity markets, and obtained similar results. Eling (2008) analyzed the behavior of different groups of CTAs in the years 1996–2005, using the classical measures of investment appraisal, such as Sharpe ratio or Markowitz model. The author concludes that the futures funds are beneficial from the investor’s point of view, taking into account both their risk-return profile and their ability to diversify the portfolio. Nevertheless, the results tend to be highly dependent on the test period. Also Jaeger (2002), Kat (2004), Laporte (2004), Lee, Koh, and Phoon (2004), Till and Eagleeye (2005), Gregoriou (2005), Avery (2006), Qureshi and Heiden (2009), Schneeweis, Crowder, and Kazemi (2010), as well as Mellin (2010) emphasize the benefits of investing in the managed futures for both the optimization of the portfolio and as independent investment. * *

*

To summarize the research on the legitimacy of the investment in the managed futures we should pay attention to several important issues. First, the

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research results are characterized by a relatively high ambiguity. Most studies have demonstrated the advantages and merits of direct investment in the CTAs, which cannot be said about the CPOs. Second, the analyses focused on different periods differ from each other in terms of results. This may be related to dynamic changes of a systemic nature that have taken place in the financial markets in the last decade. As a result, the problem of usefulness of the investments based on the commodity market to optimize the investment portfolio has yet not been resolved.

Chapter Four Financialization of Commodity Markets The process that led the world economy to the global financial crisis, as well as its explosion, was accompanied by an unprecedented increase of prices in the commodity markets. The boom in the years 2002–2008 was exceptional on many levels: in terms of its length, the scale of the increases, and in the scope of markets and instruments that it concerned. The commodity boom raised food problems in many developing countries (TDR 2008; Food & Water Watch 2009; Gayi 2012), and later, the sharp drop in prices was a key channel that transmitted the economic downturn from the developed countries to the developing countries (TDR 2009). In the recent past, the prices of many commodities saw significant increases (see figures 4.36–4.41), and then saw significant declines. Some of these increases can be attributed to the fundamental relations between supply and demand; however, the scale of the price increases itself, as well as the unprecedented increase in activity among the financial investors in the commodity markets that accompanied it, steered many researchers toward the thesis that these were the investors that were partially responsible for the untypical behavior of commodity prices. This thesis has gained such popularity that it has become widely accepted not only in the world’s scientific literature, but also among the world’s policy makers and publicists. There are plenty of examples: -The speculators have been accused of causing an increase in the prices of commodities, especially food, and thus leading to food crises in many countries in 2007–2008, as proven both in popular reports and books; for example, “Casino of Hunger: How Wall Street Speculators Fueled the Global Food Crisis” (Food & Water Watch 2009) and “Endless Appetites: How the Commodities Casino Creates Hunger and Unrest” (Bjerga 2011). - French president Nicolas Sarkozy asked rhetorically in 2011, “Speculation, panic and lack of transparency. Is this the world we want?” (Sarkozy 2011). - In 2009, a US Senate Subcommittee prepared a report on the effects of “excessive speculation” in the wheat market. The report showed special concern about the growing importance of index investments that were supposed to lead to an increase in grain prices (United States Senate Permanent Subcommittee on Investigations, 2009).

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- In the common position delivered in a report of on January 22, 2011, 48 ministers of Agriculture gathered at a meeting in Berlin stated that they were “concerned that excessive price volatility and speculation on international agricultural markets might constitute a threat to food security” (Federal Ministry of Food and Agriculture 2011) The most famous manifesto of fear over the growing importance of investors in the commodity markets is the following statement by Michael W. Masters, manager and partner at Masters Capital Management, LLC, before the US Senate Committee of Homeland Security and Government Affairs: “You have asked the question ‘Are institutional investors contributing to food and energy price inflation?’ And my unequivocal answer is YES” (Masters 2008). These words are usually referred to as “Masters Hypothesis” and are a frequent subject of discussion in recent literature dedicated to the commodity markets. The increased presence and importance of financial investors in the commodity markets is commonly defined as “financialization” (the term was coined by Domanski and Heath in 2007).1 Financialization is a multithreaded phenomenon characterized by a number of effects that may be relevant to investors in the commodity markets (Cheng and Xiong 2013). This chapter consists of three parts. First, structural changes that have taken place in the commodity markets in recent years and that are directly or indirectly associated with the phenomenon of financialization have been discussed in detail. These effects are: - the significant increase in volumes and turnover in the commodity markets - an increase in the activity and in the number of open positions in commodities futures contracts with distant maturities - an increase in the share of electronic transactions in exchange trading - changes to the composition of the market participants: increased involvement of the financial investors - the emergence of a new group of investors in the form of passive index investors Further, the phenomena that are the potential consequences of financialization are explored. These include: - the direct impact of the investors on prices in the market, and hence, on their volatility, - changes in the term structure of the markets that affect the roll yields and risk premiums, - an increase in coefficients between the different commodities and between commodities and stocks that leads to a decrease in their diversification features,

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- changes in the behavior of commodity prices may have implications for tactical asset allocation in commodity markets, - decline in the profitability of active investment strategies (technical analysis) in the commodity market. At the outset, it should, however, be stressed that the very existence of a cause-and-effect link between financialization and these phenomena, and sometimes even the mere fact that these phenomena really occur, is still the subject of lively scientific discussion. The final section of this chapter presents a detailed empirical analysis of the implications of financialization for the investors who engage strategic assets into the commodity markets, including, in particular, the decreased roll yields and the increased correlation with the stock markets. Its purpose is to illustrate the scale of changes in the markets, which may potentially have a considerable importance for the financial investors active in the commodity markets.

Structural Changes in the Commodity Markets Structural changes that have taken place in the commodity markets in recent years concern a number of previously identified areas. This part is devoted to detailed discussion and presentation of these effects.

Increased Turnover in the Commodity Markets Financial investors wishing to get direct exposure to the commodity market may accomplish it by investing in the spot or futures market. Investments in the spot market are relatively less popular (TDR 2009) and they could contribute to price increases to a small extent, except in relatively small markets such as ores (Koh 2007). The second way to get exposure is by investing in the futures market with derivatives: options, futures contracts, swaps, etc. The increased size of the commodity derivatives market, measured with both the number of open positions and the turnover, is probably the most visible aspect of changes to the commodity markets. Interestingly, this phenomenon applies to organized markets as well as OTC trading. The outstanding amount of trading with derivative instruments that are not quoted in the public markets (the so-called over-the-counter or OTC) between 1998 and 2008 increased over thirty times, but, after the outbreak of the Global Financial Crisis, the volume shrunk back to the level of 2005 and 2006. Figure 4.1 presents the base value of open positions on swap and forward contracts, and figure 4.2 expands the group of the analyzed OTC

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5,000,000

Other commodities Precious metals Gold

500,000

50,000 1998 1999 2001 2002 2003 2005 2006 2007 2009 2010 2011 2013

Figure 4.1 Notional outstanding amounts of OTC forwards and swap instruments in the world in the years 1998–2014 (USD million). Source: Bank of International Settlements, http://www.bis.org/statistics/derstats.htm.

100,000,000

Other commodities Precious metals Gold

10,000,000

1,000,000

100,000 1998 1999 2001 2002 2004 2005 2006 2008 2009 2011 2012

Figure 4.2 Notional outstanding amounts of OTC forwards, swap instruments, and options in the world in the years 1998–2014 (USD million). Source: As for figure 4.1.

instruments by options. It still appears that the peak took place about the time of an outbreak of a Global Financial Crisis. The increase in denomination of the OTC instruments caused their value to also increase, which is illustrated by the graph in figure 4.3. In tandem with the growing trade in OTC, the activity of investors in the organized markets has steadily increased. There is, however, a significant difference; although the market volume of OTC derivatives declined after 2008, the instruments listed at some regulated markets were still

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10,000,000 Other commodities Precious metals Gold 1,000,000

100,000

10,000 1998 1999 2001 2002 2004 2005 2006 2008 2009 2011 2012

Figure 4.3 Market value of OTC derivatives in the world in the years 1998–2014 (USD million). Source: As for figure 4.1.

140

1200 Contracts outstanding 1000

Trading volume

800

120 100 80

600 60 400 200 0 1993 1994 1996 1998 1999 2001 2003 2004 2006 2008 2009 2011 2013

40 20 0

Figure 4.4 Number of open positions in and trading volume of commodity futures contracts in the years 1993–2014 (million). Source: As for figure 4.1.

characterized by a further increase. Figure 4.4 illustrates the number of open positions in—and trading volume of—commodity futures contracts. When analyzing these changes, it is also worth noting that the commodity derivatives market grew significantly faster than the derivatives markets based on other assets. As a result, its share in trading volume clearly rose, which is illustrated pretty well in figure 4.5. The dynamics of the development of commodity exchanges were particularly elevated after 2004. The increase in turnover in the commodity markets

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was clearly higher than the increase in the relative economic activity during this period. Examples are provided by Bastourre, Carrera, and Ibarlucia (2008) and Domanski as well as by Heath (2007), who have pointed out that in the years 2002–2005, financial activity in the markets of copper and crude oil rose faster than the world production of raw materials at the time. As a result, the ratio of the value of commodity derivatives to the value of the annual production during 2009–10 reached a level that, at first glance, may raise an intuitive concern. The values are, for example, (Gayi 2011; Dwyer, Gardner, and Williams 2011): – 10:1 for oil, – 55:1 for sugar, – 52:1 for gold, – 76:1 for copper. These changes are so disturbing that some scholars see a clear relationship between the volume of turnover and volatility. Bessembinder and Seguin (1993) suggest that higher turnovers usually tend to be related to increased volatility. However, some reassuring aspects of the changes are highlighted by Irwin and Sanders (2012). They noted that the turnover generally increased in proportion to the number of open positions, so the ratio of the two values remained relatively stable. For example, the chart below shows the ratio of trading volume to the number of open positions in the soya, rye, and corn markets. As it is not hard to notice, from 2000 to 2011, no clear and general upward or downward trend in the described ratio is visible (figure 4.6.)

14 12

Number of contracts outstanding Turnover (contracts)

Percent

10 8 6 4 2 0 2003

2004

2005

2006

2007

2008

2009

2010

Figure 4.5 Share of instruments based on commodities in the global derivatives market (%). Source: Mojarov (2013), slide 11.

107

11.0 10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 January 2000 July 2000 January 2001 July 2001 January 2002 July 2002 January 2003 July 2003 January 2004 July 2004 January 2005 July 2005 January 2006 July 2006 January 2007 July 2007 January 2008 July 2008 January 2009 July 2009 January 2010 July 2010 January 2011 July 2011

Ratio

Financialization of Commodity Markets

Month Soybeans

Wheat

Corn

Figure 4.6 Ratio of the volume of futures contracts to the number of open positions on the cereals markets in the years 2000–2011. Source: Irwin and Sanders (2012), p. 376.

Increase in Share of the Electronic Transactions Irwin and Sanders (2012) have pointed out that another important change in the commodity markets was a transition from the traditional open-outcry system, usually associated with colorfully dressed brokers crying out in the exchange pit, to the electronic trading systems. Particularly interesting is the fact that the change in the trading system with a 150-year history was very fast: just about three to four years. Even in the years 2002–2005, only about 2 percent of the turnover on the futures markets was generated in electronic form. Dynamic growth started in 2006. Let us examine it, for example, on the soy market. In July 2006, electronic trading accounted for just 5 percent of the overall volume. Just 18 months later, it was 80 percent (Irwin and Sanders, 2012). While some of the other markets experienced a slower rate of change (the live cattle market, for example), the pace of change was still quite impressive. This is illustrated in figure 4.7, which presents changes to the grain markets in the years 2004–2011. Existing studies suggest that changes in electronic trading have had a rather positive impact on the markets, from the perspective of their players. Researchers primarily emphasize the improved liquidity and decreased transaction costs (Shah and Brorsen, 2011; Frank and Garcia, 2011). The efficiency of the markets can also not be ignored (Ates and Wang, 2005).

The Financialization of Commodity Markets

108 100% 90% 80%

Percent

70% 60% 50% 40% 30% 20% 10% 0% Jan-04

Jan-05

Jan-06

Jan-07

Corn

Jan-08 Month Soybeans

Jan-09

Jan-10

Jan-11

Wheat

Figure 4.7 Percentage of futures transactions in the cereal market made via electronic platforms (2004–2011). Source: Irwin and Sanders (2012), p. 378.

Changes to the Composition of the Market Participants Changes in the structure of the players in the futures market in the last decade took place mainly in two aspects. The first plane is the increased involvement of the purely financial market players; that is, investors who neither consume nor produce the commodities listed in the exchanges. The involvement of these investors increased at the expense of business participants (producers, processors, etc.) and other small players who had not yet reported their activities. The analytical calculation of the shares of groups of investors is not an easy task, but a hint may be the data collected by the Commodity Futures Trading Commission (CFTC), which regulates and oversees the US futures contract market. The CFTC divides the investors into two groups: commercial participants, where open positions are used to protect the existing exposure, and non-commercial ones, if that were not the case. The CFTC then provides the data on aggregate positions held by each group of investors. Additionally, it also provides information on spread traders and nonreporting market participants. Unfortunately, this simple division, although it gives some insight into the structure of the market actors, does not fully reflect its current complexity. Traditionally, the companies producing or trading with commodities were deemed market participants that used futures contracts to hedge their assets. In spite of this, some investors that are classified as commercial investors are actually dealers or funds. In response to

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109

400,000 350,000

All positions (long, short, and spread) Long positions

300,000 250,000 200,000 150,000 100,000 50,000 0 1986 1988 1991 1994 1997 2000 2003 2005 2008 2011

Figure 4.8

Non-commercial investors in the CBOT wheat market.

Source: Author’s elaboration based on CFTC data.

1,400,000 1,200,000

All positions (long, short, and spread) Long positions

1,000,000 800,000 600,000 400,000 200,000 0 1986 1988 1991 1993 1996 1998 2001 2004 2006 2009 2011

Figure 4.9 Non-commercial investors in the WTI crude oil market. Source: Author’s elaboration based on CFTC data.

these allegations, in 2007, the CFTC began providing supplementary data for twelve agricultural commodities, which also indicates index traders. The charts presented here (figures 4.8–4.12) represent the growing numbers of financial investors in sample futures markets. The illustrations show the number of open positions held by non-commercial investors. Depending on the particular case, the last decade resulted in a significant, or even unprecedented, increase in the activity of financial investors. The increase in the involvement of financial investors in the commodity markets was clearly faster than the development of these markets themselves. Let us look just at one of the most important raw materials—crude oil. The shares held by individual groups of investors in the years 1995–2009

The Financialization of Commodity Markets

110 160,000 140,000

All positions (long, short, and spread) Long positions

120,000 100,000 80,000 60,000 40,000 20,000 0 1989 1992 1994 1997 1999 2001 2004 2006 2009 2011

Figure 4.10 Non-commercial investors in the COMEX copper market. Source: Author’s elaboration based on CFTC data.

450,000 400,000

All positions (long, short, and spread) Long positions

350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 1986 1988 1991 1994 1997 2000 2003 2005 2008 2011

Figure 4.11 Non-commercial investors in the COMEX gold market. Source: Author’s elaboration based on CFTC data.

is shown in figure 4.13. The percentage of investors increased from less than 20 percent in the mid-90s to almost 80 percent in 2008. It should be admitted that the oil market is special, because the financial investors did not increase their involvement in all commodities so quickly and to such a high level. Nonetheless, an overall systemic upward trend was present in most markets. It is illustrated quite well in the graph by Zaremba (2014b), who aggregated the number of open positions held by non-commercial investors and commercial investors in 26 commodities. Their share increased from 23 percent in early 1986 to almost 45 percent in 2013 (figure 4.14). The second aspect of changes is the increase in the number of open positions in futures contracts with long maturities, that is, at the distant ends

Financialization of Commodity Markets 1,600,000 1,400,000

111

All positions (long, short, and spread) Long positions

1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 1990 1992 1994 1996 1998 2001 2003 2005 2007 2009 2011

Figure 4.12 Non-commercial investors in the NYMEX natural gas market. Source: Author’s elaboration based on CFTC data.

90%

Market share

80% 70% 60% Dec 21, 2000: CFM A signed into law

50% 40% 30% 20%

0%

March 17, 1995 September 17, 1995 March 17, 1996 September 17, 1996 March 17, 1997 September 17, 1997 March 17, 1998 September 17, 1998 March 17, 1999 September 17, 1999 March 17, 2000 September 17, 2000 March 17, 2001 September 17, 2001 March 17, 2002 September 17, 2002 March 17, 2003 September 17, 2003 March 17, 2004 September 17, 2004 March 17, 2005 September 17, 2005 March 17, 2006 September 17, 2006 March 17, 2007 September 17, 2007 March 17, 2008 September 17, 2008 March 17, 2009

10%

Index, 2008 = 100

150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 0

100%

Non-commercial share Index of total open interest, 2008 = 100

Figure 4.13

Commercial plus non-reportable share WTI Index, 2008 = 100

Market Composition, Open Interest, and the Price of Oil.

Source: Medlock and Jaffe (2009).

of the forward curves. For example, the percentage of open positions on contracts with long maturities in corn markets increased from 6 percent in 2000–2003 to 14 percent in 2009–2011, from 3 percent to 8 percent on the soy market and from 2 percent to 10 percent in the rye market (Irwin and Sanders, 2012). This phenomenon can be attributed to, among other things,

The Financialization of Commodity Markets

112 90% 80% 70% 60% 50% 40% 30% 20% 10%

Non-commercial Commercial

0% 1986 1988 1990 1992 1994 1996 1999 2001 2003 2005 2007 2009 2012

Figure 4.14 Structure of the investors in the commodity markets in the years 1990–2012. Source: Zaremba (2014b).

the increased involvement of investors in the futures markets, because they often seek additional profitability in the hitherto poorly exploited market segments. The consequences of changes in the activity of the investors in the commodity markets may be significant. They will be presented later in this book, along with arguments and evidence in support of, and against, individual theses.

Development of Index Funds The last important element of structural changes in the commodity markets is the emergence of completely new tools and investment vehicles, which granted access to the commodity markets to the masses of ordinary investors. New investment products are responsible for the massive inflow of funds into the commodity markets. Barclays Plc. (Tian 2013) estimates that at the beginning of 2013, assets to the amount of USD 400 billion were under the management of commodity funds. This increase occurred primarily due to the emergence of passive investment products, including, primarily, index funds (mainly the ETF. Index funds), which did not exist on commodity exchanges several years ago, currently have become so essential an element of the commodity markets that the CFTC has decided to classify them as a separate category in the ongoing statistics. Figure 4.15 shows the share of the index funds in the wheat markets in the years 2004–2011. As we can see, the engagement of the investors in the commodity markets is varied. At the end of 2011, it fluctuated at around 15 percent of the soy and corn market and 25 percent of the rye market. Although this 15 percent

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30%

Percent

25% 20% 15% 10% 5% July 2011

January 2011

July 2010

January 2010

July 2009

January 2009

July 2008

January 2008

July 2007

January 2007

July 2006

January 2006

July 2005

January 2005

July 2004

January 2004

0%

Date Soybeans

Corn

Wheat

Figure 4.15 Percentage of open positions held by the index investors in the years 2004–2011. Source: Irwin and Sanders (2012).

does not seem like a significant volume at first glance (additionally, we cannot see a suggestive upward trend in the graph for the years 2005–2011), it is worth remembering that these investors were practically absent less than 15–20 years ago. In other words, their involvement has increased by 15–25 percentage points in just a few years. Such a significant structural change may raise concerns about the potential consequences for financial markets.

Consequences of the Financialization to the Commodity Markets A significant increase in the activity of investors in the commodity markets may raise legitimate concerns about their impact on market operation. These concerns have been divided below into several categories and will be discussed in turn.

Increase in Volatility of the Financial Markets In discussing the consequences of financialization, it must first be noted that the potential impact, that is, the consequences of financialization, depend

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on the behavior of these investors. According to the TDR (2009), one can distinguish three main groups of financial investors in the commodity markets, who use separate strategies and different ways to influence the market: traditional investors, technical investors, and index investors. Traditional investors use fundamental information about the market to predict future market conditions. They focus on price as a function of supply and demand. Traditional Investors who depend on their market expectations can open both long and short positions, and thus profit from both rising and declining markets. The activities of traditional investors help improve market liquidity, although it may sometimes also increase its volatility. This occurs in situations where, due to rapid changes in market prices, they are forced to close their positions. Technical investors base their decisions on technical analysis that, in most cases, is associated with a momentum strategy; that is, trend-following. In the commodity market, they are represented primarily by the CTA investors and the managed futures funds. Just like traditional investors, they may take both short and long positions and make profits on declines and rises. These funds usually improve liquidity in the market. The impact on market volatility is generally assessed as ambiguous. On the one hand, in case of a sudden movement in the market, automated trading systems can activate the stoploss orders, which create additional supply or demand and thus increase the movement range even further. On the other hand, the weightage given to individual instruments that employ technical analyses in the portfolios of funds, is usually constant. This means that declines will cause the fund to increase its engagement and increases would cause it to be reduced. The result should be the stabilization of prices. The third category is represented by the index investors, whose portfolios are designed to follow a particular index at the lowest possible cost. These investors hold large, long, and passive positions and the most favorable environments for them are increasing markets and markets in backwardation. Positions are not taken based on technical or fundamental variables, but based on the weights of the defined commodities in the appropriate index. The role of index investors in enhancing market liquidity is debatable since large passive positions held until their maturity date may act like a sponge that absorbs the liquidity of the market. Similarly, a debatable question is whether these investors stabilize the market or increase its volatility. In the event of rapid declines, the investors allocating their resources in these funds may decide to withdraw their money. Such a mechanism will push funds to continue closing their positions and thus increase market declines. In turn, the weight of each commodity in the index is generally rigid, which means that the funds will be forced to periodically rebalance the portfolio. They will sell the instruments in case of a significant growth or buy up instruments in case of a decline, thus stabilizing the prices. The considerations made above do not produce a definitive picture of the relationship between the presence of speculators in the markets and their

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volatility. This state of affairs is also confirmed by empirical studies, which do not view an increase as a non-ambivalent consequence of the presence of financial investors (Brorsen and Irwin 1987; Dale and Zyren 1996; Irwin and Yoshimaru 1999), while some analyses even show a decrease in volatility (Boyd, Buyuksahin, Haigh, and Harris 2010; Brunetti and Reiffen 2013). The hypothesis regarding increased volatility is confirmed by, for example, Basak and Pavlova (2013), who, upon testing their model, show that financialization leads to an increased volatility in the prices of commodities included in the indices. A similar phenomenon is also confirmed by Liu, Qiu, and Tang (2011) as well as Adams and Glück (2013). Varadi (2012) provides evidence that this effect is not limited to mature markets, but is also present in, for example, India. Nevertheless, Sanders and Irwin (2011b) show a clear tendency to a decrease in volatility in twelve agricultural commodity markets and two markets of energy raw materials, to which the appearance of index investors leads. Similar conclusions have been drawn by Gilbert and Morgan (2010), by Bohl and Stephan (2013), as well as by Ali and Vercammen (2012). In conclusion, it seems that the current state of research does not give a clear answer to the questions of what the relationship between the volatility of commodity prices and the structure of the players in the market is, whether it exists, and how it is shaped. We can safely assume that there is a need for further research in this area.

Formation of Price Bubbles The formation of price bubbles in the commodity markets, for which investors are held responsible, is probably the catchiest potential effect of financialization. Unfortunately, in spite of a fairly general opinion that the investors are actually to blame for the drastic increases and decreases in commodity prices, this issue is not definitely resolved. According to the theory, the perpetrators of price bubbles would primarily be index investors, and two mechanisms would be responsible for that. First, the index investors generate an additional demand for commodities, which causes an increase in their prices. Second, the price increases due to demand from the index investors may be perceived by technical investors as a formation of a trend and become the basis for a decision on taking long positions. This generates additional demand, which puts a further positive pressure on prices (the so-called feedback trading). In other words, it is likely that the increased presence of financial investors in the commodity markets will lead to price increases, which do not result directly from the fundamental premises. The discussion of this issue in the prevalent scientific literature is very vivid. We can distinguish two main strands.

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Proponents of the Masters Thesis The first strand includes the literature and research confirming the supposition that financial investors should be blamed for excessive price movements in commodity markets. The argument about “unreasonable” price increases is often used in relation to the resource boom, which ended in 2008. Kaufmann (2008), for example, has built a model that explains the behavior of oil prices, taking into account a number of factors: supply and demand from the OECD countries, capacity utilization of the refineries in the OECD countries, and the slope of the forward curve, which represents the incentive to build up an inventory in the future. The model explained the behavior of prices very well by mid-2007. After this period, the prices predicted by the model continued to increase, but in fact, the prices were growing even faster. Kaufmann interpreted this situation as a price increase due to the demand from investors that could not be explained by the fundamental factors. The statistical evidence linking the presence of investors in the commodity markets with price behavior is also ambiguous. Tang and Xiong (2012) have analyzed the relationship between commodity prices and capital inflows into investment funds. The analysis is based on the reports of the Commodity Index Traders (CIT), which are published by the CFTC and allow us to estimate what funds have flown into the commodity market after 2006. The reports take index investors in twelve agricultural commodity markets into account: corn, soy, wheat from Chicago, wheat from Kansas, soybean oil, coffee, cotton, sugar, cocoa, half-carcasses, and two types of livestock. These goods are in line with the compositions of the most common indices for the commodity sector. Tang and Xiong (2012) have calculated the total exposure of the index investors in these markets through the accumulation of monthly changes in the dollar value of the net (long positions minus short) position of the investors. Figure 4.16 presents the results of the analysis by Tang and Xiong. Calculations made by researchers show a clear correlation between the financial means invested by the index investors and the prices in the market. Additionally, Bastourrevet al. (2008) have noted that in the markets, the relationship between aggregated net positions held by the investors and the evolution of market prices is visible. A graphical illustration of this observation on the example of the copper market is shown in figure 4.17. Similar conclusions that index funds have the potential to affect prices are favored by Gilbert (2009, 2010); Einloth (2009); Inamura, Kimata, Kimura, and Muto (2011). Interesting research has been conducted by Henderson, Pearson, and Wang, who focus on the Commodity Linked Notes (CLNs) and demonstrate that there is a direct relationship between inflows of funds into these investment vehicles and increases in market prices. Two approaches seem to be the most popular within the research methodology. The first approach is related to the classical Granger tests (1969) to prove that price changes are caused by changes in the involvement of investors in the market. It is applied, for example, by Singleton (2014) as

117 20

200 Cumulated index flow

10

150

S&P GSCI Agriculture and Livestock Excess Return index

100

50

0

Index flow ($ billions)

S&P GSCI Agriculture and Livestock Excess Return index

Financialization of Commodity Markets

–10 06

07

08

09

10

Figure 4.16 Cumulative index flow and S&P GSCI Agriculture and Livestock Excess Return Index, January 2006–October 2009. Source: Tang and Xiong (2012), p. 60. Notes of the authors: “This figure depicts the cumulated index flow to the 12 agricultural and livestock commodities covered by the CFTC’s CIT report, together with the S&P GSCI agriculture and livestock excess return index. We computed weekly flow to each commodity according to Equation 1 and the cumulated flow to each commodity by adding the weekly flow from the first week of 2006 to a given week. By summing the cumulated flows to the 12 commodities, we obtained the cumulated index flow.”

500

40,000

450

30,000

400

20,000

350 10,000

300 250

0

200

–10,000

150

–20,000

100 Net non-commercial positions (RHS) Copper prices (USD, LHS)

50 0 2007

Figure 4.17

–30,000 –40,000

2008

2009

2010

2011

Copper prices against net positions of the noncommercial investors.

Source: Author’s calculations based on data from Bloomberg and CFTC.

well as Hamilton and Wu (2014a), who have shown that a 13-week change in positions of the index investors were predictive of changes in oil prices in 2006–2010. It should, however, be noted that in the literature, there are many who opine that tests of this type are not suitable for analyses of the

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financial markets. Pagan and Schwert (1990) have demonstrated that the stock markets do not have the formal properties required for Granger tests to be reliable. Philips and Loretan (1994) extended this analysis to the commodities to show that prices are too variable to allow the use of Granger tests. The second approach is based on constructing econometric models that use different types of fundamental variables to try to explain the behavior of commodity prices. The researchers, applying the described models, try to calculate whether all price changes can be explained with only fundamental factors or whether, to some extent, (what extent?) this phenomenon is caused by financialization. Basak and Pavlova (2013), following this path, suggest that financialization was responsible for 11–17 percent of the increases in commodity prices during the boom in the years 2006–2008. Similar methods have also been applied by Juvenal and Petrella (2012) and Baker (2014). Gilbert and Pfuderer (2014) as well as Liu et al. (2011) suggest that, although the impact of financialization was important, these were actually some other fundamental factors which played the first fiddle. Opponents of the Masters Thesis Not all researchers agree with this view. Professors Scott H. Irwin and Dwight R. Sanders are sometimes indicated as leading opponents. In their working paper of 2010, they note that, first of all, the commodities do not constitute a market in which the probability of the bubble is high. Futures contracts are instruments with a limited investment horizon and no restrictions on short sales, so any “revaluations” of prices could be quickly eliminated. Long-term price bubbles are more likely when there is no possibility of easy arbitrage, as in the case of the real estate market. (Patel, Zeckhauser, and Hendricks 1991). Also, in theory, the introduction of the futures markets would reduce rather than increase the likelihood of bubbles (Forsythe, Palfrey, and Plott 1984; Noussair and Tucker 2006). Interesting observations are also made by Krugman (2008c) and Smith (2009), who believe that, if speculative activity lead to increase of the prices above a fundamental level, a consequence should be a state of imbalance in the market. As a result, market participants should accumulate stocks. This phenomenon was actually not observable during the last huge commodity boom. In the years 2007–2008 no above-average growth of commodity stocks in the markets was seen. Generally speaking, the results of theoretical studies and studies using more appropriate methodologies demonstrate the lack of—or only scant— evidence that “speculators” may cause any deviation of the quotes from their “foundations” and thus, cause the formation of price bubbles (Krugman 2008b; Irwin, Sanders, and Merrin 2009; Buyuksahin and Harris 2009; Stoll and Whaley 2010; Sanders and Irwin 2010; Sanders and Irwin 2011a,b; Stoll and Whaley 2011). Some their arguments will be presented below. Although the correlation between the positioning of investors and the behavior of prices is visible to the naked eye, it is still not determinative

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119

evidence that financial investors cause irrational price increases in the market. This is due to the fact that the correlation only shows the relationship between two variables and does not prejudge the nature of cause and effect. In other words, it is difficult to determine whether price increases are caused by the increased activity of the investor or if it is the rising prices that encourage the investors to increase their commitment to the market. Bastourre et al. (2008) undertook to resolve this doubt. The authors have calculated the correlation coefficient between the positioning in the period T and the rates of return in the same period, as well as in the earlier and later periods. Correlation analysis has shown that the positioning of investors is more strongly correlated with the past than with future price movements. This suggests that the investors often take decisions to increase or decrease their commitment based on changes in prices, rather than the prices changing as a consequence of changes in investors’ positions. Granger tests did not unambiguously confirm the thesis that investors affect price movements. Granger tests, the results of which have suggested that changes in the involvement of the investors cannot explain price fluctuations, have also been analyzed by Brunetti and Buyuksahin (2009), Brunetti, Buyuksahin, and Harris (2011), Buyuksahin and Harris (2011), Capelle-Blancard and Coulibaly (2012), Irwin and Sanders (2012), and Irwin et al. (2009). Sanders and Irwin (2011) emphasize that the involvement of the index investors increased about 2–3 years before the so-called bubble of 2006–2008, so that could not be responsible for it. What’s more, Tran (2012) recognizes that in case of certain commodities, such as grain, the share of the index investors in the total turnover in the years 2006–2012 actually decreased, so it is hard to blame them for the increases due to excessive demand. The conviction of the growing role of financial investors in the commodity markets as a factor responsible for the rise in commodity prices during the last decade has one major gap. If the financial investors in the futures markets were responsible for the increases in prices, one would reasonably expect that the commodities, which do not have futures markets, would be characterized by smaller price increases. This, however, has been repudiated by calculations made by Deutsche Bank (2008), which show that the prices of commodities not listed in the futures markets rose more than the listed ones did in the period from 2001 to 2008. Additionally, the chart below, in turn extends the computations from the above-mentioned analysis up to 2011. The unlisted commodities did not clearly dominate the listed ones, but neither does the reverse argument hold true (figure 4.18). An interesting illustration is also shown by Tran (2012), who overlaid the indices of unlisted and listed commodities and noted that they were generally experiencing similar changes (figure 4.19.). Existing literature seems to accept the fact that the recent years have seen changes in the commodity markets, especially in the structure of the futures market, and a growing correlation, between both individual commodities and between commodities with other asset classes (e.g. Schwindler 2007;

The Financialization of Commodity Markets

120 1400 1200

Non-exchange traded commodities

1000

Exchange traded commodities

Percent

800 600 400 200

Figure 4.18 (percent).

Natural Gas

Zinc

Aluminium

Nickel

Crude oil

Lead

Copper

Tin

Gold

Silver

Rhodium

Ruthenium

Rice

Cobalt

Manganese

Ferrochrome

Steel

Tungsten

Iron ore

Cadmium Molybdenum

0 –200

Changes in the prices of selected commodities in the years 2000–2011

Source: Author’s computations.

170

Rind index Broad commodity index

150 130 110 90 70 50 2007

2008

2009

2010

2011

2012

Figure 4.19 Traded vs. non-traded (RIND Index) commodity prices (index, rebased end-2007=100). Source: Tran (2012), slide 16.

Silvennoinen and Thorp 2009; Mayer 2010; Tang 2011; Inamura et al. 2011; Tang and Xiong 2012). However, opponents of the thesis of financialization also suggest a number of other explanations for the changes, which are not associated with the phenomenon of financialization. It turns out, however, that the cited alternative explanations cannot withstand the weight of empirical evidence. The two most common explanations for higher prices in the commodity markets in the period from 2006 to 2008

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are the growth of emerging economies and the development of the biofuel market. Growth of the Emerging Economies Krugman (2008a), Hamilton (2009), and Kilian (2009) were of the opinion that the increase in commodities was caused by the unprecedented growth of emerging economies (e.g., China, Brazil) in the first decade of the twentyfirst century. It generated a massive demand for all groups of commodities, thus causing synchronized increases in their prices, correlating with increases in the stock markets. Such a statement is supported, among other things, by a growing correlation between commodity prices and the stock indices of stock exchanges in emerging markets as well as a weakening correlation between commodity prices and the exchange rate of the US dollar (Tang and Xiong 2012). China has definitely played a major role among the emerging developing economies in the last decade, thus Tang and Xiong (2010) compared the behavior of prices in the world commodity markets with prices in China. Their observations have led them to several conclusions. First, some commodity prices are in fact following the global trends; however, the correlation was limited (e.g., the prices of heating oil behaved in such a manner). Second, many commodities in the Chinese market did not record any significant increase in price in the years 2006–2008 nor any decrease until 2009. This holds true for, among other commodities, wheat, corn, and cotton. In other words, there is reason to believe that the thesis of the significant increase in prices being due to increased demand from China is not true since a corroborating price increase has not been seen in China’s domestic market. While the average correlation between eight commodities in the global markets analyzed by Tang and Xiong and calculated based on annual periods of daily returns increased from 0.1 in the years 2000–2006 to 0.5 in the years 2008–2010, in China, it remained at 0.1. In summary, it is likely that the argument that the growth of emerging economies is responsible for the increased correlation between commodity prices does not reflect the whole truth. On the other hand, it should be accepted that there is a possibility that such an increase not being discernible might also be a matter of price regulation in China that causes domestic demand to not be adequately reflected in local prices. Biofuels Tang and Xiong (2012) also state that the biofuel policy might be to blame for the growing correlation. For example, in the United States, in order to reduce dependence on oil, laws aiming at increase the consumption of ethanol to 7.5 billion gallons in 2012 and to 36 billion gallons in 2022 were implemented. Similar regulations have been implemented in the European Union. The combination of subsidizing ethanol production by increasing the oil prices led to a situation, in which ⅓ of US corn production in 2009 was dedicated to the production of ethanol. Therefore, it seems reasonable to assume that the increased demand for corn increased its prices.

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This hypothesis has been verified by Roberts and Schlenker (2010), who carried out a quantitative analysis of the impact of changes to the energy policy on the food prices. In their opinion, the consequence could be an increase in prices by about 20–30 percent, while the quotations of corn in the period from 2006 to 2008 increased four times. What’s more, ethanol production cannot explain a synchronized boom in other soft commodity markets, such as coffee and cocoa, nor the correlations between commodities composing the indices. In conclusion, despite the common belief that the increased activity of the investors in the financial markets can be a reason for the formation of speculative bubbles and irrational price increases, the existing studies do not allow us to unambiguously confirm this thesis. This issue is still the subject of vivid debate, but so far, it looks like the pendulum swings towards opponents of the thesis that index investors are responsible for the price increases in commodity markets.

Rising Correlations in the Commodity Markets The problem of the growing interdependence of financial markets can be divided into two separate issues. The first one is the growing correlation between individual commodities and the second one is the increased link with other asset classes, particularly with the stock market. The increasing correlation between changes in the prices of individual commodities may be due to the fact that financial investors in the commodity markets often do not have expertise in particular commodities, so they would rather invest in entire baskets or indices. As a result, if these investors expect declines, for example, in the oil market, they reduce their positions in the entire index and put sale pressure on, for example, metal markets. Price decreases and increases are transmitted to various commodity classes, although nothing might happen in a given period that could have an impact on their foundations. This relationship is particularly clear for commodities that have the largest and the smallest weights in the indices, like, for example, the energy resources (especially oil) and agricultural raw materials. Growing correlation between different groups of commodities is a subject of, among others, TDR (2009) as well as Tang and Xiong (2010). Below (figures 4.20–4.23), the author’s calculations inspired by the previous research of Tang and Xiong (2010) are presented, which show a one-year rolled correlation, calculated based on daily rates of return between the WTI crude oil market and soy, cotton, cattle, and copper. Those markets are largely influenced by different fundamental factors; hence theoretically, the correlation between them should not be high; nonetheless, in recent years, it has grown significantly. All calculations are based on the indices created on rolling contracts with the shortest term to expire.

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123

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1989 –0.10

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

–0.20 –0.30

Figure 4.20 Linear correlation coefficient between WTI oil and soy in the years 1988–2011 (annual periods, daily rates of return). Source: Author’s elaboration based on data from Bloomberg.

0.50 0.40 0.30 0.20 0.10 0.00 1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

–0.10 –0.20 –0.30

Figure 4.21 Linear correlation coefficient between WTI oil and cattle in the years 1988–2011 (annual periods, daily rates of return). Source: Author’s elaboration based on data from Bloomberg.

Although the analysis shows that the correlation began to weaken after the global financial crisis, it is still higher than in previous years. Tang and Xiong (2012) emphasize another interesting phenomenon. If the increased correlation were a consequence of the activities of the financial

The Financialization of Commodity Markets

124 0.80

0.60

0.40

0.20

0.00 1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

–0.20

–0.40

Figure 4.22 Linear correlation coefficient between WTI oil and copper in the years 1988–2011 (annual periods, daily rates of return). Source: Author’s elaboration based on data from Bloomberg.

0.60 0.50 0.40 0.30 0.20 0.10 0.00 1989 –0.10

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

–0.20 –0.30

Figure 4.23 Linear correlation coefficient between WTI oil and cotton in the years 1988–2011 (annual periods, daily rates of return). Source: Author’s elaboration based on data from Bloomberg.

investors, it would be seen especially in those groups of commodities that were included in the indices. Hence, the researchers have calculated the average correlations in both groups. The results of their analysis are shown in Figure 4.24.

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0.6 0.5

Correlation

0.4 0.3 0.2 0.1 0 –0.1

75

80

85

90

95

00

05

10

Average correlation of indexed commodities Average correlation of off-indexed commodities

Figure 4.24 1973–2011.

Average correlations of indexed and off-index commodities,

Source: Tang and Xiong (2012), p. 65. Notes from authors: “This figure depicts the average return correlations of commodities in the S&P GSCI and DJ-UBSCI and commodities off these indices. We separated the samples of indexed and off-index commodities. In each sample, we constructed an equal-weighted return index for each commodity sector. A commodity is not included in the index until its average daily futures trading volume in a given calendar year is larger than 20 million USD. Then, for both indexed and off-index commodities, we computed the equal-weighted averages of the one-year rolling return correlations of all sector pairs.”

The chart presented above seems to confirm the supposition formulated by Tang and Xiong (2012). In the past ten years, the correlations between the indexed commodities have increased much more than the correlations between the commodities not included in the indices. The second part of the problem of growing interdependence in the markets is the growing correlation with the behavior of other asset classes, primarily stocks. A large number of publications on the benefits of reducing portfolio risk through diversification have led to the growing popularity of commodities among the institutional investors: investment funds, pension funds, and even endowment funds. This phenomenon is well illustrated by the fairly well known example of the funds of Harvard University (Faber and Richardson 2009). The evolution of strategic asset allocation in the years 1995–2010 could be found in Harvard Management Company’s annual reports. The share of commodities in the strategic allocation of the portfolio in the years 1995–2010 has more than doubled.2 The increased presence of investors in financial markets might have led to structural changes in the correlation between the commodities and traditional asset classes and to lower diversification capabilities than it presents in popular analyses (Buyuksahin, Haigh, and Robe 2008; Chong and Milfre 2008).

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Changes in the correlation between commodities and other asset classes are caused mainly by a mechanism related to strategic asset allocation. As more and more investors hold similar portfolios consisting of stocks, bonds, and commodities and, at the same time, try to maintain a relatively stable asset allocation, the external shocks that cause an outflow of capital will push them to sell all asset classes. In turn, the increased inflow of funds will also result in greater demand for all asset classes (Kyle and Xiong 2001). As a result, we should expect the correlation between commodities and other assets to grow. In many studies (Strongin and Petch 1995, 1996; Gorton and Rouwenhorst 2006; Armstead and Venkatraman 2007; Kat and Oomen 2007a,b) commodities are seen as an asset class that is the beneficiary of economic growth. That gives it features similar to those that characterize the stock market, and distinguishes it from, for example, those attributable to the bonds market, which are seen as security during an unfavorable economic situation. As a result, the demand for commodities should be synchronous with a demand for stocks and inversely proportional to a demand for bonds. This would mean an increase in the correlation of commodities with the stock market—as this asset class becomes more and more popular among the investors—and a decrease in their correlation with the bond market. The rising correlation between the stock and commodity markets has been noted, among others, by Tang and Xiong (2012). Similar conclusions have also been drawn by Silvennoinen and Thorp (2009), who analyzed the correlations between 24 individual commodities and key stock indices in the United States, Great Britain, Germany, and France, based on weekly data from May 1990 to July 2009. The study has shown an increasing co-integration of different classes of financial assets, even after taking into account exogenous factors such as increased volatility in the financial markets. This phenomenon has also been noted by Tang (2011) as well as Inamura et al. (2011). Confirmation that it is the financialization that is the source of the growing correlation between assets, can also be found in studies by Buyuksahin and Robe (2011); Li, Zhang, and Du (2011); Girardi (2012); Kuralbayeva and Malone (2012); Basak and Pavlova (2013); and Bicchetti and Maystre (2013). With regard to changes in the correlation of rates of return in the commodity markets, there are mainly two alternative explanations in the literature: the phenomena related to the global financial crisis in the years 2007–2008 and inflation fluctuations. Financial Crisis In the financial markets, a tendency to increase the correlation of assets during periods of financial market tensions and intense declines is widely known. This phenomenon is jokingly called “investment Murphy’s law,” which postulates that the diversification of the portfolio operates always, except where it is needed. Therefore, one of the explanations for the increase in the correlation between the commodities, in recent years, is the global

Financialization of Commodity Markets 90 80 70

127 0.7

VIX Index (LHS) Correlation of S&P-GSCI Energy and S&P-GSCI Agriculture (RHS)

60

0.6 0.5 0.4

50 0.3 40 30 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0.2 0.1 0.0 –0.1

Figure 4.25 Volatility of stock prices and correlation coefficient in the commodity markets in the years 2000–2011. Source: Author’s calculations based on data from Bloomberg.

financial crisis in the years 2007–2009 and the related increase in the volatility of the financial markets. This hypothesis can be pre-verified by the popular VIX index. The VIX index illustrates the implied volatility of short-term options on the S&P 500 quoted in the Chicago Board Options Exchange. The graph of the VIX index compared against the rolled annual correlation coefficient between the S&P GSCI Energy and S&P GSCI Agriculture indices (both are spot indices) are shown in figure 4.25. As shown in the figure above, the correlation in the commodity markets began to increase during the period 2004–2007, while the VIX index still remained at very low levels. Although the financial crisis itself coincided with a further increase in the correlation, the correlation remained at a much higher level than at the beginning of the decade even after it ceased, when, in the years 2010–2011, the VIX returned to low values. In summary, the increase in volatility associated with the financial crisis is not a sufficient explanation for the growing dependency between individual commodities. More formal proof carried out by means of regression testing can be found in Tang and Xiong (2012). Inflation Fluctuations Another group of explanations focuses on high inflation, which, on the one hand, is a consequence of the growing prices of commodities, and on the other hand, it encourages investors to invest in this market because the commodities are seen as an asset class that is positively correlated with inflation (see, for example, Gorton and Rouwenhorst 2006; Adams, Fuss and Kaiser

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Inflation rate in USA (CPI headline, LHS, %) Standard deviation of inflation rate (%, RHS)

4.00 3.50

12 3.00 10 2.50

8

2.00

6 4

1.50

2 1.00 0 1973 1976 1978 1981 1984 1987 1989 1992 1995 1998 2000 2003 2006 2009 –2 –4

0.50 0.00

Figure 4.26 Inflation and its variance in the United States in the years 1973–2011. Source: Author’s calculations based on data from Bloomberg.

2008). However, this thesis also raises many doubts. Figure 4.26 shows inflation in the United States (commodities are quoted in US dollars) and its variance calculated based on three-year periods. As can be seen in the figure presented above, the highest level and volatility of inflation in the United States was recorded in the 70s and the 80s, when the correlation in the commodity markets remained low. In the last decade, in turn, inflation has even been seen to change to deflation and the variance been seen to increase whenever a growth spurt was recorded; but that happened later than the growth in the correlation between commodities. In other words, inflation is not a sufficient explanation for the increasing interdependence of prices in the commodity markets.

Changes in the Term Structure of the Markets and the Decreased Risk Premium Passive investments in futures contracts in the commodity markets can be divided into three separate, not fully correlated sources of profit. The first one is the change in spot prices in the commodity market (the so-called spot yield), the second one is the rate of return on bonds or cash instruments constituting a security margin or a futures collateral on the futures contract (the so-called collateral yield), and the third one is roll yield, connected with the fact that futures contracts have a defined maturity, so in order to hold a position, one must open a new contract. The last-mentioned component

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15 10 5 0 1970–2000

2000–2010

–5 –10 Collateral yield

Roll yield

Spot yield

–15

Figure 4.27 Sources of profit in the commodity futures market in the years 1970– 2010 (percentage points). Source: Montier (2010).

is inextricably linked to the structure of the forward curve, which is also suggested by the previously described basic theories of formation of the forward curve (theories of insurance, theories of hedging pressure, and theories of rational expectations). The presence of financial investors may have a significant impact on the structure of the futures curve. The index Investors almost exclusively adopt long positions in accordance with the design of the index, regardless of any fundamental and technical reasons, by investing not in the spot markets, but in the futures markets (mainly in the most liquid and nearly expiring series). As a result, it may be expected that an additional demand generated by index investors may affect the term structure of the market, causing an increase in futures prices with relation to spot prices. Such a state of affairs would be even more likely if the investors had expectations regarding a further increase in commodity prices. As a result, the growth of financialization of markets could lead to a decreased roll yield. This phenomenon has been noticed by many researchers and market practitioners. Jensen (2010) and Wray (2009) have noted that the significant extension of the period in which the oil market was in contango in the years 2007–2008, as compared to earlier periods, may suggest the excessive optimism of investors about future price increases. A similar conclusion has been drawn by Montier (2010), who examined changes in the term structure of markets and noted that 24 of the 29 markets he analyzed were dominated by contango, although a common structure in their pasts was backwardation. In addition, the author measured the average profits of the previously described three sources of returns related to commodities: spot yield, collateral yield, and roll yield in the years

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All Non-financialized Financialized

64 32 16 8 4 2 1 1987 0.5

1990

1993

1996

1999

2002

2005

2008

2011

Figure 4.28 Cumulative returns on strategies based on term structure in financialized and non-financialized markets. Source: Zaremba 2014b, p. 26.

1970–2010, broken down into sub-periods 1970–2000 and 2000–2010. The results are shown in figure 4.28. Similar conclusions have been drawn by the analyst at UBS bank (UBS 2011), who noted that a lot more commodities were in contango for much of the period 2006–2010. UBS blamed pressure from the index investors for this state of affairs, and the effects seen by that organization included negative profits from futures rollover. The decreased roll yield in the markets in recent years has also been noticed by Schwindler (2007) and Mayer (2010). In the years 1970–2000, almost 20 percent of the annual returns on the investment in the futures market resulted from the roll yield and the dominant component was a collateral yield. In the first decade of the twenty-first century, the situation changed dramatically. Collateral yield was responsible for a modest percentage of the profits, a dominant component was the spot yield, and the roll yield turned out to be negative. Interesting conclusions have also been drawn by Tang and Xiong (2012). They examined the change in the forward curve for 16 non-energy commodities and oil in the years 1998–2004, and after 2004 (the year 2004 has been designated as a demarcation line, after which a significant increase in the popularity of index investment in the commodity markets has been seen). Despite the fact that a fall in the roll yield has been proven by this study, it is questionable whether it is statistically significant. A significant decrease in roll yields or risk premiums on the commodity markets has also been confirmed by Hamilton and Wu (2014b), Frenk and Turbeville (2011), Vdovenko (2013), and Zaremba (2014a). At the same time, it is also worth noting that there are also studies that undermine the impact of changes in the structure of market participants on the

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term structure and risk premiums (Irwin et al. 2011; Dwyer, Holloway, and Wright 2012; Garcia, Irwin, and Smith 2014). The decrease in roll yields may have implications for not only strategic, but also tactical asset allocation. An important strategy is based on the shape of the term structure in commodity markets.3 The concept underlying this investment technique is related to the hedging pressure hypothesis (Keynes 1930; Working 1949; Hirshleifer 1990; Basu and Miffre 2013), which tries to explain the shape of the term structure and the source of risk premium in commodity markets. If short positions are taken by the majority of hedgers, a downward pressure might be exerted on futures prices, resulting in a downside sloping of the curve. The beneficiaries of such backwardated markets are speculators taking long positions and harvesting risk premiums. On the other side, the prevalence of long speculators in a market might imply a contango situation and result in abnormal returns for speculators taking short positions. A hypothetical investor might benefit from such a situation, for example, by overweighting commodities with the most downward sloping curves and going short in the most contangoed markets. The validity of such a dynamic approach is proved in numerous studies (de Roon, Nijman, and Veld 2000; Erb and Harvey, 2006a; Basu and Miffre, 2013). The performance of the strategy based on term structure may be negatively influenced by the a large number of speculators in the market. Such situation might lead to a decrease in the ratio of speculators to hedgers. This, in turn, might imply a decrease in the risk premium transferred to an average noncommercial market participant. In fact, it turns out that the term-structure strategy actually performed worse in the financialized markets than in nonfinancialized markets. Figure 4.29 comes from a study by Zaremba (2014b) 32 16

All Non-financialized Financialized

8 4 2 1 1987

1990

1993

1996

1999

2002

2005

2008

2011

0.5

Figure 4.29 Cumulative returns on strategies based on momentum in financialized and non-financialized markets. Source: Zaremba (2014b), p. 27.

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and shows a cumulative return on the described strategy (long positions in backwardated markets and short in markets in contango). The non-financialized markets delivered markedly better rates of returns and outperformed the financialized markets by over 12 percentage points per annum over the period 1987–2013. The performed review bears two important inferences for market practitioners. First of all, commodity investments may be not beneficial in the low roll yields environment, but may still turn attractive if roll yields enter their positive territory anew. This is an interesting observation as, at the end of 2013 and at the beginning of 2014, roll yields were again positive contributors in terms of some commodities (Johnson, and Sharenow 2013; Greer and Walny 2014). Second, investors should pursue strategies that are targeted at maximizing roll yields. This can be achieved either through dynamic allocation in commodity markets or by choosing a commodity index specially designed to maximize roll yields. Such issues are further discussed, for example, by Campbell & Company (2014) or Greer, Johnson, and Worah (2012). In conclusion, the question of a changed roll yield may have a potentially significant impact on the validity of investing in commodities. The latter part of this book is aimed at assessing how big the decline of the roll yield has been in recent years and analyzing the validity of investing in commodities, assuming that the lower roll yield—considering that it is the result of excessive pressure from the investors—will not cease in the following years.

Commodities and the Business Cycle The research on the macroeconomic determinants of rates of return in the commodities market has a long history in the literature on the subject.4 Two aspects seem to be discussed most extensively: behavior of the commodity prices during the business cycle and their ability to hedge against inflation. Interestingly, in both cases, the characteristics of the commodities significantly differ from the characteristics of stocks and bonds. With regard to the business cycle, a widespread view is that the fundamental difference lies in the fact that the valuation of stocks and bonds is anticipative, while commodity prices, to a greater extent, are based on the current situation (Anson 2009, p. 332). The value of stocks and bonds is the result of evaluation of the future financial situation made by stocks and bonds issuers. The better the condition of the company, the higher are the cash flows it generates. As a result, the prices of these assets should be highest as a result of best future business prospects rather than as a result of even the best current situation. On the other hand, commodity prices present a different pattern. Demand for goods from the real economy is biggest when economic activity is at its highest. As a result, the prices of commodities are determined much more by the current economic situation than by the future. According to this reasoning, the prices should be

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at their lowest when economic activity is weakest and highest when it is strongest. In addition, it should be underlined that the rates of return on both asset classes (stocks and commodities) are usually positively correlated with changes in the economic activity, this correlation, however, has different roots. In the case of stocks, these are changes in the financial health of companies, fluctuations in discount rates, wealth effect, variable cost of financing, etc. As for commodities, the most important aspect is the current demand from the actual economy. As a result, although there may be some differences in the nature of dependencies (anticipative, coinciding, and delayed), both asset classes have a positive correlation with economic fluctuations. The theory described above is consistent with empirical observations. Adams et al. (2008) show that the rates of return in the commodity markets are characterized by positive correlations and betas with changes to industrial production in the economy. The relationship is particularly strong in the case of industrial metals and energy resources. Gorton and Rouwenhorst (2006) analyze the return on commodities and other asset classes during different phases of the business cycle. The researchers have shown that investment in the commodity markets usually brings the highest rates of return during the late phase of economic recovery, while stocks usually do so at the beginning. Some calculations even suggest that decent rates of return in the commodity markets may persist for several months or quarters after the economic growth has started to slow down (Gorton and Rouwenhorst, 2006; Anson, 2009). In other words, the rates of return in the commodity market seem to be a convergent or even delayed indicator related to the business cycle, while the rates of return on the stock market usually go a few months ahead of the business cycle (Siegel, 1991; Backus, Routledge, and Zin 2007). In economic science, there is a popular view that the nominal values of stocks or bonds fall when unexpected or actual inflation increases. The reason for this lies in the nature of these instruments. On the one hand, the bondholders receive a predefined chain of cash flows, and, apart from the amount and frequency, their current value depends on the level of interest rates, which typically rises with inflation. On the other hand, the stock prices normally represent the residual claim on the assets of the company, the value of which varies with its price level in the economy. However, a substantial part of the company’s value is a result of estimating future cash flows, the amount of which is not directly linked to the value of those assets, and the fact that companies often operate on the basis of contracts with rigidly fixed nominal rates on both the sales side (contracts with customers) and the costs side (suppliers, employees, etc.). In addition, free cash flow to shareholders, although its amount is highly uncertain, is discounted using the capital cost dependent on inflation. Unlike stocks and bonds, the nature of commodities with respect to inflation is quite different. The behavior of commodity futures is characterized by different interactions with inflation.

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Even changes to the commodity price alone affect the rate of inflation, as the commodities are a part of the inflation basket (Cheung 2009). The effect may be direct or indirect, as it is for price of oil, which is integral to the transport costs of many other commodities. What’s more, the prices of liquid futures contracts reflect the expected inflation and adapt to new market information (Adams, Fuss, and Kaiser 2008). Theoretical considerations on the role of the commodities in hedging against inflation have a solid foundation in empirical evidence. Preliminary research on the ability to invest in commodities to hedge against inflation is a subject of articles by Greer (1978) as well as Bodie and Rosanski (1980), who have analyzed the impact of changes in inflation on changes in the prices of stocks, bonds, and commodities in the years 1950–1976. They found that the excess rates of return on stocks and bonds are characterized by negative correlation coefficients with changes in inflation (−0.48 and −0.2, respectively), while the correlation coefficient with the commodities market is positive (0.52). Many subsequent studies have confirmed these findings using larger samples and more sophisticated research techniques (Gay and Manaster, 1982; Ankrim and Hensel, 1993; Froot, 1995; Becker and Finnerty, 1997; Kaplan and Lummer, 1998; Gorton and Rouwenhorst, 2006; Kat and Oomen, 2007a, b; Hoevenaars, Molenaar, Schotman, and Steenkamp 2008; Spierdijk and Umar, 2013). The correlation appears to be particularly strong with relation to unexpected inflation (Ankrim and Hensel, 1993; Gorton and Rouwenhorst, 2006) and in long time horizons (Gorton and Rouwenhorst, 2006; Roache and Attie, 2009). A significant number of studies have explored the possibility of hedging against inflation by investing in individual sub-sectors of the commodities market, or even in individual commodities (Erb and Harvey, 2006; Kat and Oomen, 2007; Woodard, 2008). Generally, it seems that the correlation is very strong in case of energy resources and industrial metals and relatively low with respect to agricultural raw materials and noble metals (Adams, Fuss, and Kaiser, 2008). Interestingly, due to the fact that most commodities included in the popular commodities indices are quoted in US dollars, the ability of commodities to hedge against the risk of inflation appears to primarily be a phenomenon related to the dollar. Many studies suggest that although the correlation coefficients with inflation in the United States are high and statistically significant, the similar dependence in Europe and Japan seems to be unclear (Adams, Fuss, and Kaiser, 2008). Nevertheless, evidence from some developing markets, such as India, proves the ability to hedge against inflation outside the United States (Joshi, 2013). The behavior patterns of the commodity prices described above were studied primarily in the period preceding the financialization of the commodities markets. The question that remains open is how the structural changes, termed financialization, have influenced these patterns. There are three important questions about structural changes in the behavior of commodities

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with relation to the macroeconomic determinants of returns in financialized commodities markets. First, because the rates of return in commodities markets and stocks have become correlated to a greater extent, it is possible that the correlation of commodities with the business cycle has been strengthened. The reasoning behind this assumption is that in the non-financialized markets, both channels of interaction have appeared at different points in time while in the financialized markets, they are synchronized, so they can strengthen each other. As a result, the question arises as to whether the linkage between rates of return in the commodities market and the business cycle is now stronger than it was before the materialization of the changes referred to as financialization. Second, due to the fact that changes in the stock and commodity prices have become more strongly correlated and more synchronized, it is possible that the traditional patterns of anticipating or converging behaviors have been disrupted. The question here is whether, in the financialized commodities markets, returns are still being delayed or converging toward economic fluctuations, or whether they behave in a more anticipatory, preemptive manner, as stocks do. Third, do the commodities markets still provide a hedge against inflation? While there is no mechanism that would ensure that financialization has a direct impact on the correlation with inflation, the effect might be indirect, for example, through interdependence with the business cycle. In short, the question is whether commodities retain their inflationary nature in both financialized and non-financialized markets. Analyses of the mentioned topics can be found in Zaremba (2014c), which focuses on the relationship between commodity prices and macroeconomic aggregates in the United States during the years 1970–2013. The commodity futures markets are an important element of portfolio management and asset allocation. Although recent and significant changes that have occurred in the commodities markets cause one to question whether commodities have retained their long-term characteristics and features, the calculations made in this book allow us to conclude that although commodities may still be used effectively in tactical portfolio allocation, their nature has changed to some extent. First, the correlation with the fluctuations of the business cycle seems to be stronger now than in previous decades. The correlation coefficients and betas have increased significantly. In other words, one may get the impression that commodity prices are now more dependent on economic fluctuations. Second, the nature of the relationship with the level of business activity has changed to some extent. It is now more similar to the stock market. In other words, over the last decade, commodity price changes have been a convergent or even anticipatory indicator, with relation to the broad economy. In general, such anticipatory behavior contradicts previous analyses

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contained in the literature, which pointed out that the commodity prices were generally delayed or convergent towards the business cycle. Third, the results of the analyses show that commodities retain their hedging properties against inflation in the face of the financialization. In fact, the correlation with changes in the CPI and the “inflation betas” has been strengthened in recent years. It can be assumed that hedging the portfolio against inflation with commodities remains completely justified. The described study, however, has one important limitation. Although it is demonstrated that the correlation of changes in the commodity market prices and economic aggregates has changed in recent years, the research has directly demonstrated the cause and effect linkages in this regard. In other words, it cannot be excluded that the observed changes were due to some other reason or that they were just a specific anomaly of the last decade. This issue should be raised in future research in this area.

Decrease in the Effectiveness of Technical Analysis In the chapter on futures contract funds, we discussed different theories and models explaining the profitability of technical analysis. It is worth noting that at least some of them can explain the recent disappearance or decrease of usefulness of that method of analysis. It is associated with the process of financialization of the commodity markets and its related phenomena. First, in the perspective of several decades, transaction costs in the financial markets have visibly decreased. Second, availability of market data as well as the ease and speed of data processing has significantly increased. Third, professionalization of the market and the investors has grown. In the 1950s, nearly 90 percent of all the stocks listed on the American market were held by individual investors. Meanwhile, in 2010, this proportion has shrunk to less than 30 percent, and the market leaders are professional financial institutions (Authers 2010, p. 10). Fourth, the role of futures funds has increased; their assets under management increased from USD 0.3 billion in 1980 to 247.8 billion in 2010.5 Fifth, the volatility of financial markets has decreased, which is not without significance, as some authors (Kidd and Brorsen 2004) point out, because futures funds are defined as long volatility strategies (Jaffarian 2009). As a result, theories and models of operating in the financial markets can explain the decrease in the effectiveness of technical analysis to some extent. Considerable controversy and differences in the assessments of technical analysis—among researchers and practitioners—have resulted in numerous studies that aimed at learning the truth about its effectiveness. These analyses are usually divided into two periods: the early studies and the current studies.6 Both categories differ mainly in the methodology, which, from the current perspective, could have given rise to many doubts in the earlier period. The exact boundary of the first period is hazy but the year 1988 is

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usually assumed to be a demarcating year, when a study by Lukac, Brorsen, and Irwin (1988) was published. It was the first study that contained many methodological improvements that corrected the key shortcomings of the research from the earlier period. The vast majority of the analyses focused on automated transactional systems based on trend following and countertrend models. Analyses that explore the predictive ability of listed formations are rare, but those can also be found (Chang and Osler 1999; Lo, Mamaysky, and Wang 2000). In view of the fact that the decreased effectiveness of technical analysis may be caused not only by factors directly related to the commodity market, but also by the broader operation of the financial markets, in this part of the book, we will also refer to the stock and currency markets. Some trends in the described markets have common characteristics. What’s more, many studies covered both commodities markets and others markets, and the methodologies and research tools in the analyses of the various segments of the financial markets have evolved in parallel. Nonetheless, in addition to presenting a broader context, the goal of this book requires drawing conclusions on the commodity market. The first widely commented upon text is a paper on the effectiveness of transactional systems published in 1960 by Donchian, who, to this date, is considered the father and guru of transactional systems (Faith 2008, p. 38; Covel 2009a, p. 52; Covel 2009b, pp. 85–89). Donchian took a closer look at the effectiveness of the breakout system, the range of which was determined by the minimum and maximum prices of the past two weeks. This system analyses the quotations of futures contracts on copper in the years 1959–1960, assuming transaction costs of USD 5.50 for a contract. In terms of a USD 1,000 collateral, the system generated a net profit of USD 3,488 for contracts expiring in December 1959, and USD 1,390 for contracts expiring one year after. The study by Donchian can hardly be regarded as sophisticated in terms of methodology, but it gave the impetus to many similar analyses. By the end of “the early period,” in 1988, over 40 analyses verifying the effectiveness of systems of moving average oscillators, breakouts, as well as various filters and stop-loss appeared. A study by Stevenson and Beara of 1970 can be considered a representative one for the early period (Irwin and Park 2008, p. 918). The analysis admittedly concerned the stock market, but it will be presented here due to a methodological contribution. The researchers analyzed three transactional systems: Alexander filters, stop-loss orders, and a combination of both strategies on the example of the corn and soy markets in the years 1957–1968. Alexander filters (Alexander 1961) establishes the simple rule of buying the instrument when its price increases by a defined x percent from the bottom, and sell it when the prices fall by x percent from the top. In turn, stop-loss orders, the originator of which is deemed to be Houthakker (1961), persuades investors to close the open positions when the price changes by a predetermined amount in a direction opposite to the expectations of the investor. Stevenson and Bear have tested the filters and stop-loss orders at

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the levels of 1.5, 3, and 5 percent, assuming transaction costs of 0.5 cents per bushel. The study brought mixed results with different combinations, but the filters generated higher profits or lower losses than the corresponding “buy and hold” strategies. The best performance was achieved by the systems with 5 percent filters. The analysis by Stevenson and Bear produced results similar to the one by Sweeny (1986), who found that the filters had generated statistically significant positive gains for ten exchange rates, while the opposite results were obtained by Fama and Blume (1966), who once tested thirty different stock markets and noted that systems based on the filters were not able to generate better results than the “buy and hold” strategies. In general, early studies suggested a greater efficiency in stock markets rather than the futures markets, because they did not bring forth abundant evidence as to the effectiveness of technical analysis for the former (van Horne and Parker 1967; Jensen and Benington 1970) and suggested the usefulness of many transactional systems in the foreign exchange and commodity markets (Poole 1967; Cornell and Dietrich 1978; Irwin and Uhrig 1984). Early studies of technical analyses had many defects (the list follows Irwin and Park (2007)). First, in most cases only one or two transactional systems that were popular during a given time were analyzed. This fact brought with it the high risk of problems such as pretest bias and data mining, which were described in the earlier part of the book. Second, in almost any case, no risk of transactional systems has been taken into account; only the profits. Third, most studies did not include formal statistical tests based on t or Z statistics, and, even if they did, their cognitive value was limited by the pretest biases and data mining (Lovell 1983; Denton 1985). Fourth, the optimization procedures may raise doubts. Most of the studies did not take into account a single predefined parameter, but only looked for the most optimal one and assumed that it would also hold good in the future. Fifth, the studies used a variety of benchmarks, but they lacked a clear consensus as to what benchmark should be used. Sixth, some analyses are difficult to interpret because the results are presented as the mean of all the analyzed filters and rules, and not as a result of various strategies and markets. Subsequent studies corrected the previous errors to large extent. With the developments in information technology, it was possible to analyze a much larger range of markets and strategies; moreover, cutting-edge research methods, such as genetic programming or bootstrapping, have been employed. Contemporary studies of technical analysis are open by Lukac et al. (1988), who studied 12 transactional systems in the commodities and financial futures contract markets in the years 1975–1984. The systems included price channels, moving averages, oscillators, filters, stop-loss orders, and combinations of the above strategies. The authors took into account the transaction costs between USD 50 to 100 per contract. The parameters of each of the systems were optimized for three years, after which the system consisting of the best parameters was tested on data from the following year. As a result, the systems were auto-calibrated and verified based on the out-of-sample data. Next, the authors conducted three statistical tests: a

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two-sided t-test to verify that the gross returns were different from zero, a one-sided t-test to verify that the net returns were higher than zero, and the Jensen’s alpha coefficients to check if the system generated above-average returns relative to the CAPM model. The T-test can be considered appropriate because the research carried out by formal tests did not allow the rejection of the hypothesis on normal distribution of returns from this strategy. The results showed that seven of the 12 systems could boast of statistically significant gross rates of return and four showed positive net returns, corresponding to annualized rates of return from 3.8 to 5.6 percent. In the same period, the corresponding benchmark strategy generated a loss of −2.31 percent annually. These systems have proved to be profitable after adjusting for risk, as they had a statistically significant Jensen’s alpha coefficient. The analyses by Lukacet al. have largely confirmed the effectiveness of technical analysis in the financial markets. The increase in computing power in the 1990s and the first decade of the twenty-first century allowed for even greater expansion in the scope of research. It is worth quoting at least an analysis of Roberts of 2005. He used a fairly complicated genetic algorithm to avoid allegations of pretest bias and data mining. In the first step, the author used 20,000 random investment rules, which then, in the 20-cycle optimization process, passed a two-year training and two-year selection period (1980–1983), and were then tested based on the period 1984–2000. The optimization criterion was the net profit (after deduction of transaction costs). The results obtained by Roberts do not present many arguments for the agitators of technical analysis. Although for 24 markets tested out-of-sample, as many as 15 managed to generate profits, but only in two cases were they statistically higher than zero. Generally speaking, a large part of the studies carried out after 1988 confirms the effectiveness of technical analysis in historical periods. An interesting summary can be found in Irwin and Park (2007), who reviewed 92 studies on technical analysis that were published in the years 1988–2004. The synthesis is presented in table 4.1. Table 4.1

Performance of technical analysis in the light of previous studies

Studies

Stock markets % Currency markets % Futures markets % Total %

The number of studies Positive

Mixed

Negative

Net profit range (out-of-sample period) (%)

26

5

12

1.1

1968−1988

60%

12%

28%

25

4

9

5–10

1976–1991

66%

11%

24%

7

1

3

4–6

1976–1986

64%

9%

27%

58

10

24

63%

11%

26%

Source: Author’s elaboration based on Irwin and Park (2004).

Study period

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Most of the texts considered by Irwin and Park wholly or partly confirm the effectiveness of technical analysis. Historically, it allowed generating the highest returns in the currency and commodities markets (the futures category consists mainly of commodities markets): an average 5–10 percent and 4–6 percent per annum, respectively. The usefulness of technical analysis in the stock markets was smaller. This effect, also with respect to the CTAs, has been noted by Zaremba (2011b). Interestingly, when we look at the changes in the effectiveness of technical analysis over time, interesting patterns reveal themselves. The situation is similar in most markets, although the commodities may seem an exception. Previous studies on the US stock market have shown that technical analysis could generate profits till the end of the 80s, but it was later not to be effective (Bessembinder and Chan 1998; Sullivan, Timmermann, and White 1999; Ready 2002). An example may be Sullivan et al. (1999), who found that short-term moving average systems generated only 2.8 percent of the average annual profit in the years 1987–1996, which, in addition, was statistically insignificant. Additionally, some researchers examined the source of profit in the futures strategies more closely. Lesmond, Schill, and Zhou (2004) have noted that the profit from the strategies based on momentum is highest for the companies that also incur the highest transaction costs. In turn, Hwang and Rubesam (2007) have come to the conclusion that the effect of momentum in the stock markets has completely disappeared after 2000. The decrease in profitability of the technical strategies may be related to an increase in the market efficiency. This explanation is also supported by the fact that in the emerging markets, which were less efficient, technical strategies proved to be profitable after 1990 as well (Ito 1999; Ratner and Leal 1999). With respect to currency markets, it seems that the period of effectiveness of technical analysis was longer (Silber 1994; Taylor 1994; Szakmary and Mathur 1997), but, as time went by, it began to decline, which has been noted by Neely and Weller (2001) as well as by Olson (2004). The latter examined 18 individual currency pairs and a balanced portfolio composed of these currencies, using the moving averages system. The portfolio saw an average rate of return adjusted for risk decreased from about 3 percent in the 70s and the 80s, down to about zero by the end of the 90s. This trend has been confirmed by Levich and Pojarliev (2008) more recently, in their study on the effectiveness of professional managers in the currency market. In the opinion of the authors, the profitability of strategies based on a trend following model had fallen significantly after 2000 and, in the years 2001–2006, the rate of return from the risk factor interpreted in such a way was only 0.05 percent per month. Shrinking profits from the currency markets can also be interpreted as an increase in the efficiency of information. The most promising are the commodity markets, as evidenced by the previously mentioned studies by Lukac et al. The specificity of this asset class has also been noticed by Schneeweis, Kazemi, and Spurgin (2008). It is interesting that in the commodity markets, even the simplest methods of technical analysis based on the momentum effect do not seem to weaken (Pirrong 2005). Erb and Harvey (2006a) analyze the momentum with respect to the

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GSCI index in the period 1969–2004. These researchers verify the effectiveness of the described methods by the trivially simple method of opening a long position in the futures contracts on the index in the month following the period in which the growth occurred, and a short position after the month when a decrease was recorded. The average annual rate of return in the years 1969–1992 amounted to 17.49 percent on long positions and 9.89 percent on short positions. In the latter period, while the profits fell, they still were significant. They amounted to 11.34 percent and 4.07 percent for long and short positions respectively. In the study by Gorton, Hayashi, and Rouwenhorst (2007) the profits from the momentum strategy based on buying commodities that had generated high rates of return and selling those that had generated low returns for the last twelve months were higher in the period 1990–2006(15.36%) than in the whole examined period from 1969–2006 (13.36%). All returns proved to be significantly higher than zero. When concluding this overview of the existing research on the commodity markets, it is worth mentioning the analysis by Miffre and Rallis (2007). The authors documented the results of the momentum strategy with regard to the thirty-one commodity futures in the years 1979–2004. The study involved the systematic buying of contracts recording highest increases in prices and selling the contracts recording highest decreases in prices in the earlier period. Sixteen variants of strategies were considered, in which periods of historical rates of return and rotation of portfolio reconstruction ranged from one to 12 months. The results obtained by Miffre and Rallis lead us to arrive at the conclusion that the momentum strategy was characterized by the ratio of the average rate of return to the average standard deviation of 0.5. This ratio remained stable in each of the next five five-year subperiods during the years 1979–2004. However, some studies also delivered contradictory results and suggested that momentum returns could also be impeded by the phenomenon of financialization. For example, it appears that the strategy of going long on the best performing markets and shorting the worst ones performed better in the markets with the low participation of financial investors. Figure 4.30 depicts the cumulative returns to the momentum strategy in 26 commodity markets for the years 1986–2012. The strategy assumed taking long and short positions in the markets with the last months’ top and bottom returns respectively. Although the results of the study that is the source of figure 4.29 are not statistically significant, they seem to be quite suggestive. The momentum strategies in non-financialized markets show cumulative returns of 896 percent over the investigated period of 1987–2013, while, in the case of financialized markets, it is only 312 percent. Summing up the discussion on technical trading strategies, we can conclude that the existing literature has largely confirmed their effectiveness in different markets by the end of the 80s or the 90s. Later, their ability to generate superior returns significantly shrunk or disappeared completely. This especially concerns the stock and currency markets. The exception to this rule proved to be the selected emerging stock markets and—crucially

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for this study—the commodities. This issue requires a more detailed quantification, which will be carried out later in the book. * *

*

In conclusion of the review of the literature made above, it seems that the likely cause of the increase in the correlation of individual commodities, changes in the term structure, and increase in the level of risk premium as well as the decrease of the effectiveness of technical analysis is financialization of the commodities markets. Nevertheless, it is important to emphasize that the whole process of cause and effect, which has led to structural changes in the commodities markets, may be much more complex. First, it is possible that the changes that have occurred in the commodities markets are the result of the cumulative effect of many different factors that are not mutually exclusive. Although the analyzed studies do not allow us to unambiguously confirm that different hypotheses are, for example, fully responsible for an increase in correlation in the commodities market; however, it does not determine the absence of their cumulative impact. In addition, their action could strengthen the effects of the financialization itself. Second, it is possible that there is a relationship of cause and effect between the very phenomenon of financialization and the different explanations for changes in the commodity markets. For example, the favorable conditions in the commodities markets could give a boost to the changes that have been multiplied by the process of financialization. Among other root causes, we could mention limited inflation in the world and a policy of very low interest rates in many countries. Nonetheless, after analyzing various hypotheses, it seems that it is the process of financialization that is largely responsible for changes in the commodity markets, which are the subject of this book; attempts to quantify them are made in the latter part of this book.

Empirical Study of Changes in the Commodity Markets The above considerations suggest that the commodity futures markets have experienced significant changes, which are the consequence of the growing importance of the financial investors, that is, the process of financialization. These changes can have a significant impact on decisions regarding strategic asset allocation in the commodity markets using passive investments, and they may relate primarily to the three following issues: ●●

●● ●●

changes in the level of correlation between the passive investments in commodities and the stock and bond market changes in the term structure of the markets decrease in the effectiveness of technical analysis

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In the following part of this book, all three issues will be subjected to empirical verification, and an attempt to quantify these phenomena will be made in order to determine the level of their potential impact on investment decisions, especially in the area of strategic asset allocation.

Increased Correlation between the Commodities and the Traditional Asset Classes The study of changes in the correlation between commodities markets and the stock and bond markets covered the period from December 31, 1991 to June 30, 2011, and was based on the monthly data sequences. The selected period was based on the availability of market data and especially the bond market indices. The survey was conducted from the perspective of a global investor who made his investments in US dollars. Therefore, the MSCI World Total Return index was chosen as a representation of the global stock market, the bond market was represented by Bloomberg/Effas US Government Bond Index 1–3. The commodities market was represented by four indices that were available in the total return version for the duration of the study: JP Morgan Commodity Curve Index, Standard&Poors Goldman Sachs Commodity Index, Dow Jones–UBS Commodity Index, and Merrill Lynch Commodity Index eXtra. The summary of the data used is presented in Table 4.2. All data used in the study came from Bloomberg, and correlation measurements are based on monthly logarithmic rates of return calculated according to equation (6): rlog = ln (

Pt ) Pt −1

(6)

where P represents the closing quotations at the end of consecutive months and rlog represents the logarithmic rate of return.

Table 4.2

Indices used in the correlation analysis

Index

Symbol

Asset class

JP Morgan Commodity Curve Index

JPMCCI

Commodities

Standard&Poors-Goldman Sachs Commodity Index

SPGSCI

Commodities

Dow Jones – UBS Commodity Index

DJUBS

Commodities

Merrill Lynch Commodity Index eXtra

MLCX

Commodities

MSCI World Total Return Index

NDDUWI

Global stock

Bloomberg/EFFAS US Government Bond Index 1–3

USG1TR

Bonds

Source: Author’s elaboration.

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1.00 0.80

JMCXTR SPGSCITR

DJUBSTR MLCXTR

0.60 0.40 0.20 0.00 1994 1996 1997 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008 2010 2011 –0.20 –0.40

Figure 4.30 Correlation coefficient between commodities and equities in the period from December 1994 to June 2011. Source: Author’s calculations.

Figure 4.30 presents rolled linear correlation coefficients calculated based on three-year periods between the stock and commodities markets in the period from December 1994 to June 2011.7 All the analyzed indices show a similar trend manifested by a systematically increasing correlation since 2004 or 2005, which reached its culmination in 2011—a peak point in the perspective of the whole examined period. It is worth noting that the years 2004 and 2005 are not negligible because they are symbolic of the beginning of an era of increased inflow of financial means into commodities funds. At the same time, these were the years with the lowest correlation in the examined period, which could provide an additional incentive for the financial investors. Table 4.6 presents a statistical analysis of the changes in the markets. The analysis conducted in a breakdown into subperiods, within which the demarcation point falls at the turn of 2004 and 2005. As already mentioned, this years are is considered the symbolic beginning of the perception of commodities as an investment asset class. Two things have contributed to this: the first publication of the famous article by Gorton and Rouwenhorst Facts and Fantasies about Commodity Futures (2004) and the increased inflows of funds into the commodities markets. For this reason, many authors assume this period to be a line break in the studies about the financialization of the commodities markets (Tang and Xiong 2012). The results of the analysis demonstrate that all the commodity indices showed, in the years 2005–2011, a clear and statistically significant increase in correlation with the stock market, both in the period from 1991 to 2004, and in the whole examined period from 1991 to 2011. The levels of the

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correlation coefficient increased from 0.8–0.18 in the years 1991–2004 to 0.59–0.62 in the period from 2005 to 2011. This state of affairs may significantly reduce the usefulness of commodities as a tool to diversify the portfolio, and should be taken into account by investors deciding on strategic asset allocation. Table 4.3 Correlation coefficients between the stock market and the commodity market in the period from 1991 to 20118 JMCXTR

DJUBSTR

SPGSCITR

MLCXTR

Correlations of returns 1991–2004 (1)

0.18

0.17

0.09

0.08

2005–2011 (2)

0.62

0.63

0.59

0.62

1991–2011 (3)

0.41

0.40

0.32

0.33

Changes in correlations (2)–(1)

0.44

0.46

0.50

0.54

4,46***

4,57***

4,47***

4,84***

(2)–(3)

0.21

0.23

0.26

0.29

z-stat

2,23*

2,38*

2,56*

2,9**

z-stat

*Significantly different than zero at 5% level. **Significantly different than zero at 1% level. ***Significantly different than zero at 0, 1% level. Source: Author’s elaboration based on data from Bloomberg.

0.60 0.40

JMCXTR SPGSCITR

DJUBSTR MLCXTR

0.20 0.00 1994 1996 1997 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008 2010 2011 –0.20 –0.40 –0.60 –0.80

Figure 4.31 Correlation coefficient between commodities and bonds in the period from December 1994 to June 2011. Source: Author’s calculations.

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Table 4.4 Correlation coefficients between the bond market and the commodity market in the period from 1991 to 2011 JMCXTR

DJUBSTR

SPGSCITR

MLCXTR

1991–2004 (1)

−0.07

2005–2011 (2)

−0.24

−0.03

0.01

−0.03

−0.20

−0.26

−0.26

1991–2011 (3)

−0.13

−0.09

−0.08

−0.10

Correlations of returns

Changes in correlations (2)–(1)

−0.17

−0.17

−0.27

−0.23

z-stat

−1.48

−1.33

−1.90

−1.79

(2)–(3)

−0.11

−0.11

−0.18

−0.16

z-stat

−0.83

−0.87

−1.37

−1.25

*Significantly different than zero at 5% level. **Significantly different than zero at 1% level. ***Significantly different than zero at 0.1% level. Source: Author’s elaboration based on data from Bloomberg.

In contrast to the stock market, the situation related to dependency on the bond market is not characterized by such an unambiguity. Figure 4.31 presents the rolled linear correlation coefficients, calculated based on threeyear periods—between the bond and commodity markets—in the period from December 1994 to June 2011.9 Correlations of commodities with the bond market have exhibited a downward trend in recent years, but they have not been as visible as with the upward trend in relation to the stock markets. This is also confirmed by a more formal statistical analysis, which is presented in table 4.7. Correlations between rates of return on the US bond market and the rates of return on the commodities market actually declined, as previously thought, but the decline was relatively small: from −0.07–0.01 in the years 1991–2004 to −0.20−0.26 in the period from 2005 to 2011. The change in the correlation, however, was not statistically significant, because it did not reach 5 percent.

Decrease in the Returns on Technical Analysis The issue of decrease in the effectiveness of technical analysis and its impact on the optimization of the investment portfolio in terms of strategy has been analyzed based on rates of return earned by active and passive indices of the futures funds. Passive indices reflect benchmark transactional systems, thus a decrease in their effectiveness will directly affect their rates of return. However, an

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Table 4.5 Indices used in the study of the effectiveness of technical analysis Index

Symbol

Type

CASAM/CISDM CTA Asset Weighted Index

CISDMCAW

Active

CASAM/CISDM CTA Equal Weighted Index

CISDMCEW

Active

BarclayHedge Trader Index

BARCCTA

Active

Mount Lucas Management Index

MLMCITR

Passive

Mount Lucas Management Total Return Index

MLMCI

Passive

Source: Author’s elaboration.

argument could be raised that in place of the old and well-recognized technical strategies represented in the benchmarks, there are new ones, more effective than their predecessors. Therefore, in addition to passive indices, active indices have been examined. The analysis covered the period from December 31, 1979, to June 30, 2011, and was based on the logarithmic monthly rates of return on the futures fund indices available during this period. Passive indices were represented by Mount Lucas Management products as total return and excess return and the passive indices included: CASAM/CISDM CTA Index in asset-weighted and equal-weighted versions as well as BarclayHedge Trader Index. A summary of the indices used is shown in table 4.5. Due to the fact that the purpose of the study presented in this book was to verify the effectiveness of technical analysis, it was decided to adjust the active indices by rate of return on liquid investments (collateral yield), in order to extract the component of returns responsible for performance of the transactional systems. The adjustments were made by subtracting the difference in rates of return of the MLM Total Return and Excess Return indices, which are correctly reflected by collateral yield (8): r* = r − (rMLMCITR − rMLMCI )

(8)

where r* and r represent adjusted and unadjusted rate of return on the analyzed active index for a given period respectively, and rMLMCITR and rMLMCI represent rates of return on the MLM indices as total return and excess return respectively. The adjusted indices will be marked with * later in the text. Figures 4.32–4.36 show the average monthly rates of return on the analyzed indices. The results shown in the charts above enable us to draw some interesting conclusions. First, upon analyzing the past 30 years, a downward trend in the graphs of passive indices is noticed. It applies to both total return and excess return indices, which suggest that decline In the interest rates is not the only factor responsible. This trend, however, is not fully confirmed by rates of return on the active indices, which, after the very successful 80s, were situated on lower but more stable levels over the next two decades.

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1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% 1984 1986 1988 1990 1992 1994 1995 1997 1999 2001 2003 2005 2006 2008 2010 –0.2% –0.4%

Figure 4.32 Average monthly rate of return on CISDMCAW* rolled over five-year periods in the years 1984–2011. Source: Author’s elaboration based on data from Bloomberg.

2.0% 1.8% 1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% 1984 1986 1988 1990 1992 1994 1995 1997 1999 2001 2003 2005 2006 2008 2010

Figure 4.33 Average monthly rate of return on CISDMCEW* rolled over five-year periods in the years 1984–2011. Source: Author’s elaboration based on data from Bloomberg.

Table 4.6 shows the statistical analysis regarding the average rates of return on the analyzed indices broken down by decades of the last 30 years. According to the earlier observations, we can again notice a decrease in performance of the indices in the years 1991–2011 as compared to the previous decade 1980–1990, although this decrease is not statistically significant, because it does not even reach 5 percent in any of any of the described cases, except MLMCITR, where, however, this fall can be explained by a decrease in market interest rates. The conducted analysis does not provide sufficient evidence that would suggest taking into account the financialization of the commodity markets

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1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% 1984 1986 1988 1990 1992 1994 1995 1997 1999 2001 2003 2005 2006 2008 2010 –0.2% –0.4%

Figure 4.34 Average monthly rate of return on BARCCTA* rolled over five-year periods in the years 1984–2011. Source: Author’s elaboration based on data from Bloomberg.

1.8% 1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% 1984 1986 1988 1990 1992 1994 1995 1997 1999 2001 2003 2005 2006 2008 2010

Figure 4.35 Average monthly rate of return on MLMCITR rolled over five-year periods in the years 1984–2011. Source: Author’s elaboration based on data from Bloomberg.

as a factor having an impact on the strategic allocation of portfolios into futures contract funds. Given the stability of rates of return, the last two decades have been the period that may well provide a basis for estimating the parameters for optimization of the portfolio, which will be discussed later in the book.

Changes in the Term Structure of the Markets Changes in the term structure of the commodity markets may be important to investors because—as the presented literature review shows—the roll

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0.9% 0.8% 0.7% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.0% 1984 1986 1988 1990 1992 1994 1995 1997 1999 2001 2003 2005 2006 2008 2010 –0.1%

Figure 4.36 Average monthly rate of return on MLMCI rolled over five-year periods in the years 1984–2011. Source: Author’s elaboration based on data from Bloomberg.

Table 4.6 Rates of return on active and passive indices of futures funds in the years 1980–2011 Period

CISDMCAW

CISDMCEW

BARCCTA

MLMCITR

MLMCI

Average monthly rates of return 1980–2011 (1)

0.53%

0.71%

0.49%

0.74%

0.31%

1980–1990 (2)

0.62%

1.28%

1.04%

1.27%

0.57%

1991–2000 (3)

0.44%

0.28%

0.09%

0.66%

0.28%

2001–2011 (4)

0.45%

0.51%

0.28%

0.24%

0.07%

1991–2011 (5)

0.42%

0.41%

0.20%

0.46%

0.20%

Differences in average monthly returns (5)–(2)

−0.20%

−0.88%

−0.83%

−0.81%

−0.37%

z-stat

−0.34

−1.37

−1.37

−3,20**

−1.47

(5)–(1)

−0.11%

−0.30%

−0.28%

−0.28%

−0.12%

−1.07

−1.08

−1.82

−0.75

z-stat

−0.40

(4)–(5)

−0.15%

0.03%

−0.17%

0.10%

0.11%

z-stat

−0.54

0.10

−0.72

0.48

0.57

(4)–(2)

−0.17%

−0.78%

−0.76%

−1.03%

−0.50%

z-stat

−0.28

−1.18

−1.23

−3,71**

−1.80

*Significantly different than zero at 5% level. **Significantly different than zero at 1% level. Source: Author’s elaboration based on data from Bloomberg.

yield is crucial from the investor’s point of view. If this component of returns were reduced due to the demand pressures of financial investors, one would expect that, ceteris paribus (with other profit components unchanged), the expected rate of return on investments in commodities would have

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Table 4.7 Indices used in the study of changes in the sources of returns in the commodity markets Index

Symbol

Investigation period

Standard&Poors-Goldman Sachs Commodity Index

SPGSCI

January 1970–June 2011

JP Morgan Commodity Curve Index

JPMCCI

December 1991–June 2011

Dow Jones – UBS Commodity Index

DJUBS

December 1991–June 2011

UBS Bloomberg Constant Maturity Commodity Index

UBSCMCI

October 1997–June 2011

Merrill Lynch Commodity Index eXtra

MLCX

December 1991–June 2011

Source: Author’s elaboration.

decreased. Below we will verify the hypothesis on shrinkage of the roll yield in recent years. The analysis of changes in sources of profit in the commodity markets has been carried out by calculating the spot yield, roll yield, and collateral yield, based on commodity market indices. The analysis has been conducted based on all available commodity market indices that were recorded in the period from December 31, 1991, to June 30, 2011, and which are calculated in all three forms (spot return, excess return, total return). As a result, the study covered five indices: Standard&Poors-Goldman Sachs Commodity Index, JP Morgan Commodity Curve Index, Dow Jones-UBS Index, UBS Bloomberg Constant Maturity Commodity Index (available since October 1997), and Merrill Lynch Commodity Index eXtra. Each index has been used in three versions: spot return, excess return, and total return, which gives a total of 15 indices. The first of these indices is the only widely published index, which has been calculated continuously from January 1970 in all three versions (spot return, excess return, and total return), and therefore, in this case, an additional analysis will be made that will cover an extended period from January 31, 1970, to June 30, 2011. This case will be discussed first. A summary of the indices used is shown in table 4.7. The study was based on monthly logarithmic rates of return on these indices, whereby the monthly returns of the individual components were calculated according to the formulae below. rsy = rSR

(9)

where rsy represents the spot yield component, and rSR represents return on the spot index; (10) rry = rER − rSR where rry represents the roll yield component and rER represents the yield on excess return index; (11) rcy = rTR - rER

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where rcy represents the collateral yield component and rTR represents the yield on total return. Figure 4.37 shows the cumulative arithmetic rates of return on individual components of profit in relation to the S&P GSCI index in the years 1970–2011. The results illustrated in the graph above allow for some interesting observations. First, most profits earned by the SPGSCITR index were not a result of changes in the spot prices, but of the collateral. Second, by the end of the 1990s, that is, for over the first 30 years of its existence, the roll

1000% 800%

Spot yield Roll yield Collateral yield

600% 400% 200% 0% 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 –200%

Figure 4.37 Cumulative arithmetic rates of return on individual components of profit on the SPGSCI index in the period from January 1970 to June 2011. Source: Author’s elaboration based on data from Bloomberg.

Table 4.8

Sources of return on the SPGSCI index in the years 1970–2011

Period

Spot return

Roll return

Collateral return

Average monthly rates of return I 1970–XII 2004 (1)

0.27%

0.16%

0.52%

I 2005–VI 2011 (2)

0.98%

−1.22%

0.18%

I 1970–VI 2011 (3)

0.38%

−0.06%

0.47%

(2)–(1)

0.72%

−1.37%

−0.34%

z-stat

0.79

−9.64

(2)–(3)

0.61%

−1.16%

z-stat

0.67

−8.22

Differences in returns

Source: Author’s calculations based on data from Bloomberg.

−14.90 −0.28% −12.63

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153

yield was responsible for a greater part of the profits than the spot yield accounted for. The change occurred in the last decade, when the roll yield transformed into significant loss and spot return turned to be a major component of the total return. Table 4.8 presents the statistical analysis of the differences in the rates of return on individual components of the SPGSCI indices. The analysis was broken down into subperiods covering the time until the end of 2004 and from the beginning of 2005 onward. 400%

Spot yield

350%

Roll yield

300%

Collateral yield

250% 200% 150% 100% 50% 0% 1992 1993 1994 1996 1997 1999 2000 2001 2003 2004 2006 2007 2009 2010 –50%

Figure 4.38 Cumulative arithmetic rates of return on individual components of profit of the JPMCCI indices in the period from December 1991 to June 2011. Source: Author’s calculations based on data from Bloomberg.

Table 4.9 Sources of return on the JPMCCI indices in the period from December 1991 to June 2011 Period

Spot return

Roll return

Collateral return

Average monthly rates of return I 1970–XII 2004 (1)

0.33%

0.03%

0.32%

I 2005–VI 2011 (2)

1.01%

−0.52%

0.18%

I 1970–VI 2011 (3)

0.56%

−0.15%

0.28%

(2)–(1)

0.68%

−0.56%

−0.14%

z-stat

0.89

−7.39

−6.24

(2)–(3)

0.45%

−0.37%

−0.09%

z-stat

0.58

−5.34

−4.21

Differences in returns

Source: Author’s calculations based on data from Bloomberg.

The Financialization of Commodity Markets

154 600% 500%

Spot yield Roll yield Collateral yield

400% 300% 200% 100% 0% 1992 1993 1994 1996 1997 1999 2000 2001 2003 2004 2006 2007 2009 2010 –100%

Figure 4.39 Cumulative arithmetic rates of return on individual components of profit of the DJUBS indices in the period from December 1991 to June 2011. Source: Author’s calculations based on data from Bloomberg.

Table 4.10 Sources of return on the DJUBS in the period from December 1991 to June 2011 Period

Spot return

Roll return

Collateral return

I 1970–XII 2004 (1)

0.51%

−0.19%

0.32%

I 2005–VI 2011 (2)

1.12%

−1.01%

0.18%

I 1970–VI 2011 (3)

0.71%

−0.47%

0.27%

(2)–(1)

0.61%

−0.82%

−0.14%

z-stat

0.86

−6.11

−6.20

(2)–(3)

0.41%

−0.55%

−0.09%

z-stat

0.57

−4.26

−4.18

Average monthly rates of return

Differences in returns

Source: Author’s calculations based on data from Bloomberg.

The data presented in table 4.8 confirms previous observations. After 2004, the roll yields have fallen significantly, both in relation to the previous period and the whole examined period, and the observed change was different from zero at a very high level of statistical significance. By occasion, it is worth noting that a very clear decline in collateral yield has also been recorded, therefore it has been associated with a reduction in the value of dollar interest rates and is not a subject of interest in this study.

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155

Spot yield Roll yield Collateral yield

300% 200% 100% 0% 1992 1993 1995 1996 1998 1999 2001 2002 2004 2005 2007 2008 2010 –100%

Figure 4.40 Cumulative arithmetic rates of return on individual components of profit of the SPGSCI indices in the period from December 1991 to June 2011. Source: Author’s calculations based on data from Bloomberg.

Table 4.11 Sources of return on the SPGSCI indices in the period from December 1991 to June 2011 Period

Spot return

Roll return

Collateral return

I 1970–XII 2004 (1)

0.36%

−0.15%

0.32%

I 2005–VI 2011 (2)

0.98%

−1.22%

0.18%

I 1970–VI 2011 (3)

0.57%

−0.50%

0.28%

(2)–(1)

0.62%

−1.07%

−0.14%

z-stat

0.64

−6.81

−6.24

(2)–(3)

0.42%

−0.71%

−0.09%

z-stat

0.43

−4.75

−4.22

Average monthly rates of return

Differences in returns

Source: Author’s calculations based on data from Bloomberg.

The following figures and tables present similar calculations for all five indices analyzed in the period from December 1991 to June 2011. The performed calculations provide arguments in favor of the earlier suggestions that the financialization and the related pressure from the financial investors in recent years have led to significant changes in the term structure of the market. As a result, the rollover incurred systematic losses for investors. If the level of financialization of the markets remains so high, the investors making decisions about strategic asset allocation should take into

156 350% 300% 250%

The Financialization of Commodity Markets

Spot yield Roll yield Collateral yield

200% 150% 100% 50% 0% 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 –50%

Figure 4.41 Cumulative arithmetic rates of return on individual components of profit of the UBSCMCI indices in the period from December 1997 to June 2011. Source: Author’s calculations based on data from Bloomberg.

Table 4.12 Sources of return on the UBSCMCI indices in the period from December 1997 to June 2011 Period

Spot return

Roll return

0.52%

−0.03%

Collateral return

Average monthly rates of return I 1970–XII 2004 (1)

0.28%

I 2005–VI 2011 (2)

1.21%

−0.37%

0.19%

I 1970–VI 2011 (3)

0.85%

−0.19%

0.24%

Differences in returns (2)–(1)

0.70%

−0.35%

−0.09%

z-stat

0.97

−4.65

−3.58

(2)–(3)

0.37%

−0.18%

−0.05%

z-stat

0.51

−2.95

−2.06

Source: Author’s calculations based on data from Bloomberg.

account that the future expected rates of return on the commodity market indices might be lower than historical ones, because of the demand pressure exerted by financial investors. However, if the demand pressure decreases in the future, the roll yield could again be an important component of the rates of return on passive investments in the commodities market. The scale of the decrease of profit, depending on the considered indices, may range from 0.35 to 1.07 percentage points per month for the period from December

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157

600% Spot yield 500%

Roll yield Collateral yield

400% 300% 200% 100% 0% 1992 1993 1995 1996 1998 1999 2001 2002 2004 2005 2007 2008 2010 –100%

Figure 4.42 Cumulative arithmetic rates of return on individual components of profit of the MLCX indices in the period from December 1991 to June 2011. Source: Author’s calculations based on data from Bloomberg.

Table 4.13 Sources of return on the MLCX indices in the period from December 1991 to June 2011 Period

Spot return

Roll return

Collateral return

Average monthly rates of return I 1970–XII 2004 (1)

0.52%

0.22%

0.32%

I 2005–VI 2011 (2)

1.13%

−0.81%

0.18%

I 1970–VI 2011 (3)

0.72%

−0.12%

0.28%

(2)–(1)

0.61%

−1.03%

−0.14%

z-stat

0.68

−8.21

−6.24

(2)–(3)

0.41%

−0.68%

−0.09%

z-stat

0.46

−6.03

−4.22

Differences in returns

Source: Author’s calculations based on data from Bloomberg.

1991 to December 2004 and from 0.18 to 0.71 percentage points for the whole period, from December 1991 to June 2011. For the JPMCCI index, which will be used for research on asset allocation within the portfolio investment in the further part of the book, the adjustments amounted to 0.56 and 0.37 percentage points per month respectively, which is 6.67 and 4.45 percentage points per annum (logarithmically).

Chapter Five Performance Measurement of Commodity Investments It is necessary to select appropriate measures to properly and fairly evaluate the validity of investment in commodity indices and managed futures. In the literature, there is a wide range of measures that are used for assessment of the financial investments. Those most commonly used, however, have been built for traditional asset classes (stocks, bonds, etc.), so they are not always fully suitable for the evaluation of alternative investments. Nonetheless, generally, the more recent measures are also not free from defects. This chapter discusses two main threads. The first one focuses on the evaluation of stand-alone investments in the commodity markets and the other on strategic allocation within the investment portfolio.

Stand-alone Investments The purpose of this section is to offer the appropriate tools to evaluate investments in commodities as stand-alone investments. The investment performance measurement ratios existing in the literature have several disadvantages that make them not completely suitable for the performance measurement of investments based on the commodities markets. Therefore, this part of the book focuses on the design of a suitable measure. The considerations in this area have been performed in three stages. The first part provides an overview of the most commonly used investment performance assessment ratios along with a description of their different variations and modifications, which are important for investments in futures contracts. Then, the most important drawbacks of the described measures, which are most visible in the context of the subject being studied, are listed. Ultimately, the two new measures that are aimed at correcting the shortcomings of their predecessors are discussed. These measures are later used in the empirical study presented in chapter 6.1

Sharpe Ratio William Sharpe, later a Nobel Prize winner, published a paper titled “Mutual Fund Performance” in 1966, in which he described an index that was later

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160

named after its creator. The Sharpe ratio is still, without a doubt, the most popular investment performance measurement tool, which takes into account not only the profit, but also the risk. Traditional Definition In the most traditional approach, the Sharpe ratio measures the excess rate of return per unit of risk taken by the investor (Sharpe 1966). The Sharpe ratio is calculated by dividing the excess return and risk, understood as the volatility (standard deviation)2 of rates of return, which is reflected in equation (12). SR =

RP − RF σP

(12)

where Rp represents rate of return on the portfolio being evaluated during the relevant period, RF represents rate of return on a risk-free instrument, and σp represents volatility of portfolio returns. All values are customarily given per annum. It is extremely important that the level of the Sharpe ratio is dependent on the length of the analyzed period (time dependent). This is because, as the analyzed period gets longer, the expected rates of return grow linearly or exponentially and the standard deviation increases approximately in proportion to the square root of the period concerned (Lhabitant 2008b, p. 455). As a result, the annual Sharpe ratio, for example, will be about 12 times greater than the monthly rate. A graphical representation of the Sharpe ratio in the graph of dependency of return on volatility is the slope of the line connecting the observed rate of return on the portfolio to the rate of return on a risk-free instrument (figure 5.1).

R

P (σP, RP) Sharpe ratio RF σ

Figure 5.1 Graphical representation of the Sharpe ratio. Source: Author’s elaboration.

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161

In the existing literature, this line is often called the capital allocation line (Lhabitant 2008b, p. 457) and can be described by the following equation: Re turn = RiskFree Re turn + ( SharpeRatio × Volatility ) The name “capital allocation line” is derived from the fact that each point corresponds to its portfolio, which is a combination of a risk-free instrument and the analyzed portfolio. Points to the left of the analyzed portfolio P are deleveraged portfolios; that is, those in which the share of P is less than 100 percent and the remainder is a risk-free instrument. On the other hand, points to the right of P are leveraged portfolios; that is, those in which additional shares in the portfolio P have been additionally bought for the funds from a loan with rate RF. Modifications of the Sharpe Ratio Market practice has developed many modifications of the Sharpe ratio (see, for example, Dowd 2000; Vinod and Morey 2001; Le Sourd 2007). The most popular ratio used as an information ratio (IR), is the Sharpe ratio. Other ratios are mainly designed to adapt the Sharpe ratio to the specifics of different asset classes. An overview of the most popular variations of the classic Sharpe ratio is given below. Sharpe Ratio as a Long or Short Position—IR Sharpe also proposed a revised definition of his ratio (Sharpe 1994). The need for a revised definition developed with the gap between the interpretations of the ratio, which in academic discourse was usually presented in an ex ante version and in practice, it was used ex post, to assess historical investment performance. The main difference in both approaches relates, inter alia, to the risk-free rate of return: In terms of ex ante, it is constant whereas in terms of ex post, it may change over time. The new definition of the Sharpe ratio is more general. It is called the IR by market practitioners: IR =

D σD

(13)

where D represents the excess rate of return above the benchmark being used, defined according to equation: D = RPt − RBt

(14)

RPt in equation (14) represents the rate of return on the analyzed portfolio over the period t, and RFt represents the return above the benchmark over the same period.

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According to the definition of 1994, the Sharpe ratio is thus the quotient of the average excess return above the benchmark D and standard deviation of the benchmark σD, as defined in equation (15): T

σD =

∑ (D t =1

t

− D)2

T −1

(15)

The new definition is much more flexible than the old one, as it shows what excess rate of return above the benchmark is possible with a given portfolio in exchange for the risk of deviations of the benchmark results. Importantly, the benchmark is not necessarily a risk-free instrument. Let us consider a hypothetical initial investment and try to find a portfolio that has better risk and return parameters. In this interpretation, a higher IR is a better “deviation” of the benchmark, because it provides an attractive rate of return in exchange for a relatively little additional risk. It is worth noting that the definition of IR is consistent with the original definition of the Sharpe ratio, as published in 1966. In situations where the risk-free rate is constant, the denominator is simplified to the standard deviation of return and the equation is identical to the definition of the traditional Sharpe ratio. The Sharpe ratio, according to the new definition, is sometimes referred to as the long/short Sharpe ratio. This concept is related to the fact that, from the investor’s point of view, excess rates of return D correspond to the strategy of a short position in the benchmark and a long position in the analyzed portfolio. Adjusted Sharpe Ratio One of the major and frequently emphasized drawbacks of the Sharpe ratio is that it assumes a normal distribution of rates of return on investment. This has led to the emergence of many new measures, which are presented later in this chapter. Pezier and White (2006) have adjusted the existing Sharpe ratio to take into account the effect of skewness and kurtosis of returns. The result of this work is the adjusted Sharpe ratio (ASR):   S  K − 3 2 ASR = SR × 1 +   × SR −   × SR     6 24  

(16)

where S represents skewness of the distribution of rates of return and K represents kurtosis. Modified Sharpe Ratio3 The traditional Sharpe ratio, which was created with a view to classic investment in the stock and bond market, does not work perfectly for the futures market. One of the problems pointed out by Schwager (1996) is the use of

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163

leverage. Since the numerator contains the risk premium, if positive, the increasing leverage, ceteris paribus, will always increase the Sharpe ratio. This is because the risk premium is increasing at a faster rate than the standard deviation of returns. Therefore, Schwager (1996) proposes a measure called modified Sharpe ratio I (MSRI), which, in the numerator, contains not a risk premium, but the average rate of return. RP σP

MSRI =

(17)

Sharpe-Israelsen Ratio Another major disadvantage of both the traditional Sharpe ratio and IR is the fact that they can give incorrect results for rates of return on the analyzed instruments or portfolios lower than the rate of return on a risk-free instrument. In such a situation, the highest Sharpe ratio does not necessarily mean the best investment. This is illustrated in table 5.1 (the example inspired by Le Sourd 2007). Fund A generated a lower rate of return than fund B, despite a higher volatility of returns similar to the benchmark and in spite of the fact that it is characterized by a higher Sharpe ratio, suggesting a better investment. Israelsen (2005) has proposed the adjustment of that anomaly of the classic Sharpe ratio and IR by raising the denominator to a power that is a quotient of the generated positive or negative risk premium and its absolute value. Keeping the earlier notation of equations (12) and (13), the appropriate modifications are represented by equations (18) and (19). SR =

IR =

RP − RF R −R

σ P RPP − RFF

(18)

D D

σD D

(19)

The IRs for funds A and B calculated in the adjusted way are −96.47 and −18.21, respectively. Fund B was rated higher, which means that the

Table 5.1

IR for negative rates of return—a calculation example Excess return over S&P500

Tracking error

IR

Fund A

−7

14

−0.5

Fund B

−4

5

−0.8

Source: Author’s computations inspired by Le Sourd (2007).

164

The Financialization of Commodity Markets

introduced modification of IR restored its ability to rate the funds from the “best” to the “worst.”

Ratios Based on the Capital Asset Pricing Model The capital asset pricing model (CAPM) was originally developed by Sharpe (1964), mainly for three purposes: to explain the reasons for portfolio diversification, to create a framework for the valuation of assets in conditions of risk for returns in a competitive market, and to explain differences in the risk premiums of various assets.4 The detailed characteristics of the Sharpe model were extensively presented in a number of studies (e.g., Newman, Milgate, and Eatwell 1992; Elton and Gruber 1995; Campbell, Lo, and MacKinlay 1997; Sharpe, Alexander, and Bailey 1998; Francis 2000; Danthine and Donaldson 2001; Cochrane 2005; Jajuga and Jajuga 2006; Wilmott 2008); however, a more detailed discussion of this topic goes beyond the scope of this book. Nevertheless, the implications of the CAPM model are reflected in the methods of performance evaluation of investment portfolio management. The fundamental assumption of the CAPM model is that the volatility of a financial instrument can be broken down into two parts: systematic risk and specific risk. Systematic risk stems from general changes in market yields and is a part of the volatility of a financial instrument that is perfectly correlated with the market portfolio. Specific risk is, in turn, associated with a financial instrument and, by nature, it is not correlated with the market. The described properties have important implications for portfolio analysis. In the process of building a portfolio, systematic risks of individual assets are simply summed up while specific risks are “diversifying each other” because they are not correlated. Therefore, in a well-diversified portfolio, the share of specific risk should be minor. An important implication of the CAPM model is that, if specific risk can be almost completely eliminated from the portfolio, the rational investors should focus on the systematic risk, which is the only significant source of volatility in a well-diversified portfolio. As a result, the investors should not be compensated for a specific risk that they can get rid of without much trouble, and the only source of risk premium should not be the volatility of the entire portfolio, but rather, the volatility of its systematic component. According to the CAPM model, the expected rate of return on the financial instrument consists of two components: the rate of return on a risk-free instrument and the risk premium; the latter component is linearly dependent on exposure to the market risk (beta coefficient of the financial instrument) and the risk premium (the expected excess return on the market portfolio return above the rate of return on a risk-free instrument). This relationship is described by equation (20). ECAPM (RP ) = RF + βP [ E(RM ) − RF ]

(20)

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165

where RP, RM, and RF represent the rate of return on the analyzed portfolio, on the market portfolio, and on the risk-free instrument respectively; βP represents the beta of the portfolio relative to the market portfolio; and E() represents an operator of unconditional expectation. Equation (20) is the most important conclusion of the CAPM model. It indicates that the expected rate of return on the portfolio does not depend on its volatility but on the exposure to the market risk. Ceteris paribus, the higher the beta coefficient of the investment portfolio, the higher the expected rate of return; similarly, a low beta coefficient should be reflected in the low expected rate of return. Graphically, the CAPM model implies that, in beta-rate of return terms, all properly priced instruments and portfolios should be placed on a straight line called the security market line (SML). It is shown in figure 5.2. The point of intersection of the SML and the R axis signals the rate of return on a risk-free instrument and its inclination angle stems from a market risk premium. The greater risk aversion manifested by investors, the steeper the SML should be, which means that a higher level of risk premium should be offered for a given level of systematic risk. When discussing the CAPM model, we need to pay attention to how the SML line is constructed. A risk-free instrument and market portfolio are at that line behind the beta coefficient, at a level of zero and one respectively. Any other portfolio on the SML line could be replicated by a combination of a risk-free instrument and the market portfolio. This important conclusion, called the two-fund separation theorem, has important implications for the evaluation of actively managed portfolios. Jensen’s Alpha According to the CAPM model, correctly evaluated assets should be on the SML line. If the instrument brings a higher rate of return than is apparent from its level of risk measured by the beta coefficient, rational investors R SML Market portfolio (1, RM)

RF β

Figure 5.2

Security market line.

Source: Author’s elaboration.

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166

should generate sufficient demand to increase the price, thus lowering the expected rate of return to a level appropriate for SML. A similar situation is in the case of instruments under SML: the investors should eliminate them from their portfolios, thus reducing their prices and raising the expected rate of return. Fund managers try to find undervalued assets. If they are successful in this, their portfolios bring higher rates of return than the level of market risk. This means that their portfolios are above the SML. In turn, inefficiently managed portfolios are below the SML. Jensen’s alpha coefficient (Jensen 1968) is defined as the rate of return earned by the fund or the instrument less the expected rate of return resulting from the CAPM model: α P = RP + ECAPM (RP )

(21)

or, in another form: α P = (RP − RF ) + βP (RM − RF )

1 2

(22)

Graphically, Jensen’s alpha coefficient is the vertical distance between the asset and the SML: the instruments with a positive alpha are above the line and the instruments with a negative alpha coefficient, below (figure 5.3). Common wisdom on the utilization of Jensen’s alpha coefficient prefers funds and instruments with higher alpha to those with a lower one. In existing literature, numerous modifications to and improvements of Jensen’s alpha coefficient can be found. Black (1972) suggested using a portfolio with a beta coefficient equal to zero instead of a risk-free return. Brennan (1970) constructed a model that took taxes into account. Elton and Gruber (1995) suggested using a total risk in place of a systematic one. Many papers also suggested giving additional attention to the way the profit was generated and how the alpha coefficient was decomposed with respect R SML Fund A αA