Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook 1484274393, 9781484274392

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Event- and Data-Centric Enterprise Risk-Adjusted Return Management A Banking Practitioner’s Handbook — Kannan Subramanian R Dr. Sudheesh Kumar Kattumannil

Event- and Data-Centric Enterprise Risk-Adjusted Return Management A Banking Practitioner’s Handbook

Kannan Subramanian R Dr. Sudheesh Kumar Kattumannil

Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook Kannan Subramanian R Chennai, India

Dr. Sudheesh Kumar Kattumannil Chennai, India

ISBN-13 (pbk): 978-1-4842-7439-2 https://doi.org/10.1007/978-1-4842-7440-8

ISBN-13 (electronic): 978-1-4842-7440-8

Copyright © 2022 by Kannan Subramanian R and Dr. Sudheesh Kumar Kattumannil This work is subject to copyright. All rights are reserved by the publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Susan McDermott Development Editor: Laura Berendson Coordinating Editors: Rita Fernando and Mark Powers Copyeditor: April L. Rondeau Martinez Cover designed by eStudioCalamar Cover image by Pixabay (www.pixabay.com) Distributed to the book trade worldwide by Apress Media, LLC, 1 New York Plaza, New York, NY 10004, U.S.A. Phone 1-800-SPRINGER, fax (201) 348-4505, email [email protected], or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science+Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation. For information on translations, please e-mail [email protected]; for reprint, paperback, or audio rights, please e-mail [email protected]. Apress titles may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Print and eBook Bulk Sales web page at http://www.apress.com/bulk-sales. Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub. For more detailed information, please visit http://www.apress.com/source-­code. Printed on acid-free paper

Table of Contents About the Authors����������������������������������������������������������������������������������������������������xv About the Technical Reviewer�������������������������������������������������������������������������������xvii Acknowledgments��������������������������������������������������������������������������������������������������xix Preface�������������������������������������������������������������������������������������������������������������������xxi Chapter 1: Commercial Banks, Banking Systems, and Basel Recommendations���� 1 1.1 Financial Markets������������������������������������������������������������������������������������������������������������������� 2 1.1.1 Currency Market (FX market, Forex market)������������������������������������������������������������������ 2 1.1.2 Money Market����������������������������������������������������������������������������������������������������������������� 3 1.1.3 Capital Market���������������������������������������������������������������������������������������������������������������� 3 1.1.4 Commodities Market������������������������������������������������������������������������������������������������������ 3 1.1.5 Exchange and the Over-the-Counter (OTC) Market��������������������������������������������������������� 3 Settlement������������������������������������������������������������������������������������������������������������������������������� 4 1.2 Commercial Bank — Lines of Business and Products����������������������������������������������������������� 5 1.2.1 Treasury — The Hub of the Bank����������������������������������������������������������������������������������� 6 1.2.2 C  orporate Banking�������������������������������������������������������������������������������������������������������� 18 1.2.3 R  etail Banking�������������������������������������������������������������������������������������������������������������� 21 1.2.4 Term Structure of Interest Rates (TSIR)������������������������������������������������������������������������ 25 1.3 S  ource Systems�������������������������������������������������������������������������������������������������������������������� 27 Introduction��������������������������������������������������������������������������������������������������������������������������� 27 1.3.1 S  pecialized Systems���������������������������������������������������������������������������������������������������� 29 1.3.2 Core Banking System��������������������������������������������������������������������������������������������������� 48 1.3.3 Domestic and International Payments�������������������������������������������������������������������������� 49 1.3.4 Systems Owned by Other Functions����������������������������������������������������������������������������� 49 1.3.5 O  ther Systems�������������������������������������������������������������������������������������������������������������� 51 iii

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1.4 Evolution of Basel Risk Management Recommendations����������������������������������������������������� 61 1.4.1 1988 Basel-I����������������������������������������������������������������������������������������������������������������� 61 1.4.2 2004 Basel II����������������������������������������������������������������������������������������������������������������� 68 1.4.3 2010 Basel III���������������������������������������������������������������������������������������������������������������� 78

Chapter 2: Siloed Risk Management Systems�������������������������������������������������������� 85 Common Functions in Risk Management Systems��������������������������������������������������������������������� 87 2.1 Treasury’s Market Risk and Credit Risk Management���������������������������������������������������������� 89 2.1.1 Treasury Risk Management System Modules��������������������������������������������������������������� 89 2.1.2 Credit Risk in Treasury Books������������������������������������������������������������������������������������� 105 2.1.3 Treasury Market and Credit Risk Measurement��������������������������������������������������������� 109 2.1.4 Performance Attribution��������������������������������������������������������������������������������������������� 130 2.2 Credit Risk in the Loan Book����������������������������������������������������������������������������������������������� 132 2.2.1 Risk Perspective of the Lending Process������������������������������������������������������������������� 132 2.3 Asset Liability Management (ALM)�������������������������������������������������������������������������������������� 147 2.3.1 A  LM Overview������������������������������������������������������������������������������������������������������������� 147 2.3.2 M  ulti-Currency ALM System��������������������������������������������������������������������������������������� 149 2.3.3 A  LM Risks������������������������������������������������������������������������������������������������������������������� 153 2.3.4 A  LM Metrics���������������������������������������������������������������������������������������������������������������� 156 2.3.5 Asset Liability Management Committee (ALCO)��������������������������������������������������������� 167 2.4 Anti–Money Laundering and Countering the Financing of Terrorism (AML-CFT)���������������� 171 International Effort for the Prevention and Detection of ML and FT������������������������������������ 172 ML-FT Risk Identification����������������������������������������������������������������������������������������������������� 173 2.4.1 R  isk Analysis and Assessment����������������������������������������������������������������������������������� 174 2.4.2 Risk Mitigation, Control Corrections, and Improvement��������������������������������������������� 176 2.4.3 T esting of Corrective Action���������������������������������������������������������������������������������������� 176 2.4.4 Residual Risk Monitoring�������������������������������������������������������������������������������������������� 177 2.4.5 T he AML-CFT Solution������������������������������������������������������������������������������������������������ 177

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2.5 Operational Risk Management (ORM)��������������������������������������������������������������������������������� 178 2.5.1 Risk and Control Self-Assessment (RCSA)����������������������������������������������������������������� 180 2.5.2 Operational Risk Case Studies����������������������������������������������������������������������������������� 182 2.5.3 Risk Monitoring���������������������������������������������������������������������������������������������������������� 198 2.5.4 Corrective Action Planning (CAP)�������������������������������������������������������������������������������� 199 2.5.5 Loss Database Module����������������������������������������������������������������������������������������������� 200 2.5.6 Economic Capital Calculation������������������������������������������������������������������������������������� 202 2.6 Siloed As-Is Risk Management Environment���������������������������������������������������������������������� 203

Chapter 3: ERRM Gap Analysis & Identification���������������������������������������������������� 205 3.1 What Caused the Siloed Architecture? What Is the Impact?����������������������������������������������� 206 3.1.1 Siloed Architecture����������������������������������������������������������������������������������������������������� 206 3.1.2 Siloed Operating Model and Risk Management��������������������������������������������������������� 210 3.1.3 BCBS 239 Is a Step Forward��������������������������������������������������������������������������������������� 217 3.1.4 Integrated Risk Management & ERRM����������������������������������������������������������������������� 218 3.2 Gap Identification���������������������������������������������������������������������������������������������������������������� 220 3.2.1 Review As-Is Operating Model����������������������������������������������������������������������������������� 222 3.2.2 Document New Business Requirements�������������������������������������������������������������������� 239 3.2.3 Review of ERRM Requirements���������������������������������������������������������������������������������� 247 3.2.4 Define ERR Conceptual Model������������������������������������������������������������������������������������ 263 3.2.5 The Gap – What Needs to Be Done?��������������������������������������������������������������������������� 266 3.3 Summary – Build & Improve Capabilities��������������������������������������������������������������������������� 281 Agile Bank of the Future Model������������������������������������������������������������������������������������������� 281 Stop the Incremental Approach to Leveraging Technology�������������������������������������������������� 282 Customer Experience���������������������������������������������������������������������������������������������������������� 283

Chapter 4: ERR Model Implementation Methodology������������������������������������������� 285 4.1 ERRM Methodology������������������������������������������������������������������������������������������������������������� 286 4.1.1 Project Governance���������������������������������������������������������������������������������������������������� 287 4.1.2 Corporate Governance������������������������������������������������������������������������������������������������ 291 4.1.3 Enterprise Risk-Adjusted Return Governance������������������������������������������������������������ 296 4.1.4 Business Architecture (BA)����������������������������������������������������������������������������������������� 311 v

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4.1.5 Enterprise Architecture����������������������������������������������������������������������������������������������� 314 4.1.6 Enterprise Data Architecture & Management������������������������������������������������������������� 316 4.1.7 Enterprise Costing Framework����������������������������������������������������������������������������������� 319 4.1.8 Enterprise Funds Transfer Pricing (FTP) Framework�������������������������������������������������� 321 4.1.9 Revision of MR, CR, ALM, and ORM Frameworks������������������������������������������������������� 322 4.1.10 Enterprise Stress Testing������������������������������������������������������������������������������������������ 332 4.1.11 Capital Adequacy������������������������������������������������������������������������������������������������������ 334 4.1.12 Enterprise Knowledge Management (EKM)�������������������������������������������������������������� 335

Chapter 5: Enterprise Architecture����������������������������������������������������������������������� 339 5.1 Ontology-Driven Information Systems�������������������������������������������������������������������������������� 341 5.1.1 Core Principles of Enterprise Architecture������������������������������������������������������������������ 342 5.2 Service-Orientated Architecture (SOA)�������������������������������������������������������������������������������� 344 5.2.1 Overview��������������������������������������������������������������������������������������������������������������������� 344 5.2.2 Features of SOA���������������������������������������������������������������������������������������������������������� 346 5.2.3 SOA Implementation��������������������������������������������������������������������������������������������������� 348 5.3 Microservices Architecture (MSA)��������������������������������������������������������������������������������������� 352 Case Studies������������������������������������������������������������������������������������������������������������������������ 354 5.4 Introduction to Cloud����������������������������������������������������������������������������������������������������������� 354 Case Study��������������������������������������������������������������������������������������������������������������������������� 355 5.5 E nterprise Event–Driven Architecture��������������������������������������������������������������������������������� 355 5.5.1 Event–Driven Architecture (EDA) Overview���������������������������������������������������������������� 355 5.5.2 Complex Event Processing (CEP)�������������������������������������������������������������������������������� 361 5.5.3 COSO Model, Event-Driven Architecture & Process Automation��������������������������������� 365 5.5.4 Offensive & Defensive Events������������������������������������������������������������������������������������� 366 5.6 E nterprise Process Automation������������������������������������������������������������������������������������������� 372 5.6.1 Process-based Operating Model�������������������������������������������������������������������������������� 372 5.6.2 BPM Suite Components���������������������������������������������������������������������������������������������� 378 5.6.3 P rocess Automation Examples����������������������������������������������������������������������������������� 383 5.6.4 Process-based Operational Risk Management���������������������������������������������������������� 433 5.6.5 Continuous Process Improvement������������������������������������������������������������������������������ 437 vi

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5.7 Robotic Process Automation (RPA)�������������������������������������������������������������������������������������� 440 Risk Management and Robotic Process Automation����������������������������������������������������������� 442 5.8 SOA–BPMS Convergence���������������������������������������������������������������������������������������������������� 442 5.9 Enterprise Cost Management���������������������������������������������������������������������������������������������� 443 Activity-Based Costing�������������������������������������������������������������������������������������������������������� 444 5.10 Gap Resolutions – Enterprise Architecture Category�������������������������������������������������������� 452 5.10.1 O  mni-Channel Platform�������������������������������������������������������������������������������������������� 452 5.10.2 F inancial Inclusion���������������������������������������������������������������������������������������������������� 453 5.10.3 Corporate Banking Improvements���������������������������������������������������������������������������� 454

Chapter 6: Enterprise Data Management�������������������������������������������������������������� 457 6.1 Data Management Frameworks������������������������������������������������������������������������������������������ 457 DAMA-DMBOK1�������������������������������������������������������������������������������������������������������������������� 458 DCAM2���������������������������������������������������������������������������������������������������������������������������������� 458 6.1.1 DAMA-DMBOK������������������������������������������������������������������������������������������������������������ 458 6.1.2 Data Management Capability Assessment Model������������������������������������������������������ 459 6.2 Enterprise Data Management��������������������������������������������������������������������������������������������� 461 6.2.1 Data Taxonomy & Ontology����������������������������������������������������������������������������������������� 462 6.2.2 Business Case for Enterprise Data Management������������������������������������������������������� 475 6.2.3 Enterprise Data Management Strategy���������������������������������������������������������������������� 476 6.2.4 Enterprise Data Model & Architecture������������������������������������������������������������������������ 483 6.2.5 Enterprise Data Management Technology������������������������������������������������������������������ 505 6.2.6 Data Management Program��������������������������������������������������������������������������������������� 523 6.2.7 Data Quality and Lineage�������������������������������������������������������������������������������������������� 525 6.2.8 Data Control Environment������������������������������������������������������������������������������������������� 543 6.2.9 Data Governance�������������������������������������������������������������������������������������������������������� 565 6.3 Reference ERRM Architecture��������������������������������������������������������������������������������������������� 573 6.3.1 Reference Enterprise Architecture for the ERR Model������������������������������������������������ 574

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Chapter 7: Enterprise Risk Data Management (A Subset of Enterprise Data Management)�������������������������������������������������������������������������������������� 579 7.1 Enterprise Risk Ontology����������������������������������������������������������������������������������������������������� 582 7.1.1 Risk Data�������������������������������������������������������������������������������������������������������������������� 582 7.1.2 Business Glossary������������������������������������������������������������������������������������������������������ 587 7.1.3 ERRM Taxonomy��������������������������������������������������������������������������������������������������������� 588 7.1.4 Risk Data Dictionary��������������������������������������������������������������������������������������������������� 598 7.1.5 Enterprise Risk-Adjusted Return Ontology����������������������������������������������������������������� 604 7.2 Ontology-based ERRM System������������������������������������������������������������������������������������������� 635 7.3 Enterprise Risk–Return Data Strategy�������������������������������������������������������������������������������� 637 7.3.1 International Effort – Data Standardization���������������������������������������������������������������� 638 7.3.2 Enterprise Risk-adjusted Performance Metrics��������������������������������������������������������� 642 7.3.3 Event-Driven Offensive and Defensive Data Management����������������������������������������� 644 7.4 Enterprise Risk Data Discovery������������������������������������������������������������������������������������������� 647 7.4.1 Risk Management Data Requirements����������������������������������������������������������������������� 648 7.4.2 Master, Meta, Reference, Historic, Time-Series, Transaction Data������������������������������ 690 7.4.3 Enterprise Data Standardization��������������������������������������������������������������������������������� 699 7.4.4 Enterprise Risk Data Catalog�������������������������������������������������������������������������������������� 700 7.5 Event-Driven, Data-Centric ERRM��������������������������������������������������������������������������������������� 701 Event-Driven Architecture (EDA), Process Automation�������������������������������������������������������� 701 Event-Driven, Threat–Asset–Vulnerability (TAV) Approach�������������������������������������������������� 703 7.5.1 E vent Driven��������������������������������������������������������������������������������������������������������������� 705 7.5.2 Risk Register & Events����������������������������������������������������������������������������������������������� 709 7.5.3 State Transitions, Actions & Events���������������������������������������������������������������������������� 713 7.5.4 Data State Transition Diagrams (DSTD)���������������������������������������������������������������������� 719 7.5.5 Process Mining & State Transition������������������������������������������������������������������������������ 720 7.6 Risk Data Management Technology������������������������������������������������������������������������������������ 722 7.6.1 T ime-Series Database������������������������������������������������������������������������������������������������ 722 7.6.2 In-memory Management and Graph Database Applications�������������������������������������� 723 7.6.3 C++, Python, R Programming������������������������������������������������������������������������������������� 727 viii

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7.7 Multi-dimensional Enterprise Risk Data Model������������������������������������������������������������������� 728 7.7.1 Adaptation of Data Point Model for Enterprise Risk Data Model�������������������������������� 728 7.8 Approach to Assessing EDM Maturity��������������������������������������������������������������������������������� 730 Major Components of the Data Governance Maturity Model����������������������������������������������� 730

Chapter 8: Data Science and Enterprise Risk–Return Management��������������������� 733 8.1 Math & Stats in Risk Data Calculations������������������������������������������������������������������������������� 736 Introduction to Different Disciplines of Mathematics and Statistics����������������������������������� 736 8.1.1 Elementary Statistics������������������������������������������������������������������������������������������������� 738 8.1.2 Distributions��������������������������������������������������������������������������������������������������������������� 745 8.1.3 Parametric Models and Non-parametric Alternatives������������������������������������������������ 780 8.1.4 Discriminant Analysis������������������������������������������������������������������������������������������������� 786 8.1.5 Deterministic, Probabilistic, Stochastic Models���������������������������������������������������������� 786 8.1.6 Receiver Operating Characteristic (ROC) Curve���������������������������������������������������������� 788 8.1.7 Line of Equality, Concentration Measures������������������������������������������������������������������ 789 8.1.8 Regression Analysis��������������������������������������������������������������������������������������������������� 791 8.1.9 Risk Management – Statistical Usage������������������������������������������������������������������������ 800 8.1.10 Data Bias������������������������������������������������������������������������������������������������������������������ 802 8.2 Theory and Concepts���������������������������������������������������������������������������������������������������������� 803 8.2.1 Uncertainty in Risk-Return����������������������������������������������������������������������������������������� 804 8.2.2 Portfolio Theory���������������������������������������������������������������������������������������������������������� 805 8.2.3 Risk-Neutral Pricing��������������������������������������������������������������������������������������������������� 807 8.2.4 Probability Theory and Information Theory���������������������������������������������������������������� 808 8.2.5 Law of Large Numbers (LLN)�������������������������������������������������������������������������������������� 810 8.2.6 The Central Limit Theorem (CLT)��������������������������������������������������������������������������������� 810 8.2.7 T he Fourier Transform������������������������������������������������������������������������������������������������ 810 8.2.8 E uler Theorem and Allocation������������������������������������������������������������������������������������� 811 8.2.9 M  arkov Chain�������������������������������������������������������������������������������������������������������������� 811 8.2.10 F actor Models����������������������������������������������������������������������������������������������������������� 812 8.2.11 Eigen Decomposition of the Covariance Matrix�������������������������������������������������������� 813 8.2.12 Stochastic Differential Equations (SDE)�������������������������������������������������������������������� 814 ix

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8.2.13 Brownian Bridges����������������������������������������������������������������������������������������������������� 816 8.2.14 Structural and Reduced Form Models���������������������������������������������������������������������� 816 8.2.15 Enterprise Cause–Event–Consequence Discovery��������������������������������������������������� 817 8.2.16 Causal Loops and TTC, TTI, and TTR������������������������������������������������������������������������� 821 8.2.17 Tail Behavior������������������������������������������������������������������������������������������������������������� 824 Theory, Concepts & Occam’s Razor Principle���������������������������������������������������������������������� 826 8.3 Risk Management Models�������������������������������������������������������������������������������������������������� 826 8.3.1 aTime-Series Models�������������������������������������������������������������������������������������������������� 826 8.3.1 bCorrelation Model Taxonomy������������������������������������������������������������������������������������ 828 8.3.2 Market Risk (MR) Models������������������������������������������������������������������������������������������� 829 8.3.3 Credit Risk������������������������������������������������������������������������������������������������������������������ 836 8.3.4 Asset Liability Management (ALM)����������������������������������������������������������������������������� 839 8.3.5 Operational Risk��������������������������������������������������������������������������������������������������������� 840 8.4 ERR Model Governance������������������������������������������������������������������������������������������������������� 841 8.4.1 Statistical Information System����������������������������������������������������������������������������������� 841 8.4.2 ERR Modeling Ecosystem������������������������������������������������������������������������������������������� 842 8.4.3 Risk–Return Model Management������������������������������������������������������������������������������� 844 8.4.4 I nterconnected Models����������������������������������������������������������������������������������������������� 853 8.4.5 ERR Model Governance���������������������������������������������������������������������������������������������� 854

Chapter 9: Advanced Analytics and Knowledge Management����������������������������� 859 9.1 Advanced Analytics������������������������������������������������������������������������������������������������������������� 859 9.1.1 Descriptive, Prescriptive, Predictive, Discovery���������������������������������������������������������� 860 9.1.2 Algorithm�������������������������������������������������������������������������������������������������������������������� 864 9.1.3 Machine Learning������������������������������������������������������������������������������������������������������� 865 9.1.4 Neural Networks (NN)������������������������������������������������������������������������������������������������� 878 9.1.5 Overfitting or Underfitting the Data���������������������������������������������������������������������������� 880 9.1.6 Deep Learning������������������������������������������������������������������������������������������������������������ 881 9.1.7 Reference Advanced Analytics Functional Architecture��������������������������������������������� 882

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9.2 Knowledge Management (KM)�������������������������������������������������������������������������������������������� 883 9.2.1 Ontology-driven Knowledge Management (KM)��������������������������������������������������������� 883 9.2.2 KM Methodology��������������������������������������������������������������������������������������������������������� 884 9.2.3 Knowledge Graphs (KG)���������������������������������������������������������������������������������������������� 892 9.3 KM and AA Applications������������������������������������������������������������������������������������������������������ 898 9.3.1 Sales & Marketing������������������������������������������������������������������������������������������������������ 898 9.3.2 Risk Profiles���������������������������������������������������������������������������������������������������������������� 899 9.3.3 Behavioral Analytics – Customer & Staff�������������������������������������������������������������������� 899 9.3.4 360° View of Human Capital (Employee)�������������������������������������������������������������������� 900 9.3.5 360° View of Customer����������������������������������������������������������������������������������������������� 904 9.3.6 Transaction Analysis��������������������������������������������������������������������������������������������������� 908 9.3.7 Enterprise Fraud Prevention��������������������������������������������������������������������������������������� 910 9.3.8 Anti–Money Laundering & Countering the Financing of Terrorism (AML-CFT)����������� 914 9.3.9 Treasury Trading & Deep Learning������������������������������������������������������������������������������ 916 9.3.10 Enterprise Liquidity Management (ELM)������������������������������������������������������������������ 918 9.3.11 W  ealth Management������������������������������������������������������������������������������������������������� 920 9.3.12 Credit Risk Management������������������������������������������������������������������������������������������ 920 9.3.13 B  anking Operations�������������������������������������������������������������������������������������������������� 922 9.3.14 Enterprise Content Management (ECM)������������������������������������������������������������������� 926 9.3.15 Banking Case Studies����������������������������������������������������������������������������������������������� 926 9.4 A  nalytics Maturity Evaluation���������������������������������������������������������������������������������������������� 930 Data Analytics Maturity Phases������������������������������������������������������������������������������������������� 931 Enterprise Knowledge Management����������������������������������������������������������������������������������� 932

Chapter 10: ERRM Capabilities & Improvements�������������������������������������������������� 935 10.1 Enterprise Liquidity Management (ELM)��������������������������������������������������������������������������� 936 10.1.1 Liquidity Assessment Principles������������������������������������������������������������������������������� 937 10.1.2 Basel III – Liquidity Risk Framework, Main�������������������������������������������������������������� 941 10.1.3 Implementing the New ELM������������������������������������������������������������������������������������� 943 10.1.4 Liquidity Stress-Testing Framework������������������������������������������������������������������������� 970 10.1.5 Developing a Contingency Funding Plan (CFP)��������������������������������������������������������� 975 xi

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10.1.6 Monitoring Intra-day Liquidity Risk�������������������������������������������������������������������������� 977 10.1.7 Introduction to Cash Flow @ Risk���������������������������������������������������������������������������� 983 10.2 Dynamic ALM�������������������������������������������������������������������������������������������������������������������� 984 Dynamic Sources & Dynamic Uses�������������������������������������������������������������������������������������� 985 10.3 Liquidity Transfer Pricing (LTP)������������������������������������������������������������������������������������������ 987 10.3.1 LTP as Part of FTP����������������������������������������������������������������������������������������������������� 989 10.4 Improvements to Balance Sheet Optimization������������������������������������������������������������������ 995 Common Equity Tier 121������������������������������������������������������������������������������������������������������� 996 Tier 2 Capital21��������������������������������������������������������������������������������������������������������������������� 996 10.4.1 Balance Sheet Projections���������������������������������������������������������������������������������������� 999 10.4.2 An Illustrative Optimization Approach��������������������������������������������������������������������� 1002 10.5 Improved Risk Measures������������������������������������������������������������������������������������������������ 1005 10.5.1 Process & Operating Model Maturity���������������������������������������������������������������������� 1005 10.5.2 Liquidity-adjusted Market Risk������������������������������������������������������������������������������� 1014 10.5.3 Liquidity-adjusted Credit Risk�������������������������������������������������������������������������������� 1018 10.5.4 Risk-Adjusted NIM�������������������������������������������������������������������������������������������������� 1020 10.5.5 Tail Behavior����������������������������������������������������������������������������������������������������������� 1022 10.5.6 Expected Shortfall / Conditional VaR���������������������������������������������������������������������� 1025 10.6 Copulas for Measuring Enterprise Risk��������������������������������������������������������������������������� 1032 10.7 Bank-wide Stress Testing����������������������������������������������������������������������������������������������� 1038 Bank-wide Stress-Testing Framework������������������������������������������������������������������������������ 1039

Conclusion���������������������������������������������������������������������������������������������������������� 1043 C1. Enterprise Approach to Maximizing Risk-Adjusted Returns��������������������������������������������� 1043 Risk Governance���������������������������������������������������������������������������������������������������������������� 1043 Single ERM Measure��������������������������������������������������������������������������������������������������������� 1043 Expected Shortfall and Backtesting���������������������������������������������������������������������������������� 1044 Risk-Adjusted NIM, Liquidity-Adjusted VaR����������������������������������������������������������������������� 1044 Enterprise Stress Testing��������������������������������������������������������������������������������������������������� 1045 Risk-Weighted Asset (RWA) Optimization�������������������������������������������������������������������������� 1045

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C2. Enterprise Architecture����������������������������������������������������������������������������������������������������� 1047 Ontology-based Information Systems������������������������������������������������������������������������������� 1047 Services and Micro-Services Architecture Orientation������������������������������������������������������ 1047 Event-Driven Architecture������������������������������������������������������������������������������������������������� 1047 C3. Technology������������������������������������������������������������������������������������������������������������������������ 1048 Robotics Process Automation (RPA)���������������������������������������������������������������������������������� 1048 C4. Enterprise Data Management Technology������������������������������������������������������������������������ 1048 Data Virtualization (DV) and Data as a Service (DaaS)������������������������������������������������������� 1048 In-Memory Computing������������������������������������������������������������������������������������������������������� 1048 Graph Database and Knowledge Graphs��������������������������������������������������������������������������� 1049 Knowledge Management��������������������������������������������������������������������������������������������������� 1049 Machine Learning�������������������������������������������������������������������������������������������������������������� 1049 AI and ML Usage Challenges��������������������������������������������������������������������������������������������� 1050 Sandbox – Modeling & Regulatory Needs������������������������������������������������������������������������� 1051 PSD2 and GDPR����������������������������������������������������������������������������������������������������������������� 1052 Statutory Audit Qualification���������������������������������������������������������������������������������������������� 1053 Polyglot Persistence���������������������������������������������������������������������������������������������������������� 1053 Data Ops���������������������������������������������������������������������������������������������������������������������������� 1053 C5. Climate Change & Banking����������������������������������������������������������������������������������������������� 1053 The Equator Principles������������������������������������������������������������������������������������������������������ 1053 C6. Data Is the Lifeblood of ERR Management����������������������������������������������������������������������� 1054

Appendix A: Abbreviations��������������������������������������������������������������������������������� 1055 Appendix B: List of Processes���������������������������������������������������������������������������� 1061 Bibliography������������������������������������������������������������������������������������������������������� 1063 C  hapter 1�������������������������������������������������������������������������������������������������������������������������������� 1063 Chapter 2�������������������������������������������������������������������������������������������������������������������������������� 1064 Chapter 3�������������������������������������������������������������������������������������������������������������������������������� 1067 Chapter 4�������������������������������������������������������������������������������������������������������������������������������� 1069

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Chapter 5�������������������������������������������������������������������������������������������������������������������������������� 1070 Chapter 6�������������������������������������������������������������������������������������������������������������������������������� 1071 Chapter 7�������������������������������������������������������������������������������������������������������������������������������� 1073 Chapter 8�������������������������������������������������������������������������������������������������������������������������������� 1075 Chapter 9�������������������������������������������������������������������������������������������������������������������������������� 1080 Chapter 10������������������������������������������������������������������������������������������������������������������������������ 1082

Index������������������������������������������������������������������������������������������������������������������� 1085

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About the Authors Kannan Subramanian R is a chartered accountant with about 35 years of experience in the banking and financial services industry, with exposure to the financial markets in the United States, Europe, and Asia. He has worked for Standard Chartered Bank and for leading banking solution companies, including the leading global risk-management solution provider, Algorithmics (now part of IBM Risk Management & Analytics). He is a consultant for System Design Consulting Prospero AG on strategic matters and on the design of risk-management solutions. He has successfully leveraged his academic and work experience in the area of banking, including risk-management and banking automation. Dr. Sudheesh Kumar Kattumannil is an associate professor at the Indian Statistical Institute in Chennai, India. His research interests include survival analysis, reliability theory, variance inequality, moment identity, estimation of income inequality measures, measurement error problems, and empirical likelihood inference. He has published on topics related to statistics, mathematics, and risk management. He is a recipient of the Jan Tinbergen Award for young statisticians (International Statistical Association, The Netherlands) as well as a recipient of an Indo-US fellowship. 

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About the Technical Reviewer Paul Cretaro is an information technology professional with over 25 years of industry experience, mostly concentrated in information security and team management. He has been CISSP certified for over 18 years and is an ISC2 exam subject matter expert for the CISSP exam. He has strong managerial skills with extensive training experience, and is an MCSE Windows expert, college-level trainer, and published author, with hands-on security lab manuals sold worldwide. Paul has also worked in the financial sector for 16 years, supporting information technology and security.  

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Acknowledgments The main challenge in providing actionable intelligence is obtaining wholesome data of good quality. I have been in the banking industry for 35 years and wish to state that the challenge has become an obstacle for business growth and risk management. Globally, this is a banking industry pain-point, and this book is an attempt to provide a resolution for it. This book is for commercial banks that buy their banking software solutions. The terms “siloed” and “legacy” are understood differently by bankers, enterprise architects, and data scientists. This book interprets them in the context of hard-­ wired systems and data governance. It makes a case for interoperable, loosely coupled, component-based enterprise architecture. The “time-gap” in the evolution of the banking business model, banking technology, and Basel risk-management recommendations is the primary cause for the siloed environment. It is not a result of a mistake made by the bank. However, the delay in making the decision to transform the siloed environment into an agile, bank-of-the-future environment is hurting banks. Banks should stop the incremental approach to automation and transform their processing environment at the enterprise level. Risks are inherent in all decisions. A consensus is required in the understanding of all banking terms for maximizing risk-adjusted returns. This is the primary driver for the evolution of ontology-based data management methodologies and systems. This book recommends managing risk incidents as risk events. As such, they can be classified as known or unknown events. In an event-driven architecture, it is possible to identify and manage data streams before and after the event, with the objective of preventing risks or maximizing risk-adjusted returns. The concept of system dynamics includes causal loops. The loops highlight the flow of data in an event cycle. The causal relationships— the data stream prior to the risk events and the consequences of a risk event—have a degree of complexity attached to them. Banks can immensely benefit from process automation, including robotic process automation, in a services- and event-based environment. Banking data is non-fungible. Banks should focus on data flows rather than on storage. When there is a consensus on their meaning, relationships between data can be defined accurately and business process flows can be aligned with data flows. xix

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Data governance is a joint effort between bankers and the technology team. Data lineage can easily be established in a services-based, event-driven, process-­automated environment. This is not a book on risk modeling. The main focus is not on risk quantification either. Risk data characteristics and usage are different from accounting data. Bankers and data scientists should understand the nuances of risk data management. This is the reason for providing various statistical explanations, models, and examples. I thank my co-author for his contribution in this regard. This book provides examples, case studies, methodologies, and techniques for operationalizing an enterprise risk-adjusted return model. It recommends that banks use the graph database, in-memory management, and knowledge graphs for efficient management of enterprise data. The capabilities explained in this book will help a bank govern its data and use a single view of truth for maximizing risk-adjusted returns. I am grateful to Anand Krishnamurthy, Ramesh SundaraRajan, George Sequeira, N. Sudhakar, and Mahesh Iyer for their support. They are senior bankers with more than three decades of experience. I also acknowledge the contribution of Dr. Ms. C. Selvaraj for some of the technology-related points in the book. This is my fourth book on risk management. It would not have been possible for me to meet the exacting standards of my publishers without encouragement from my daughter, Radhika, and wife, Geetanjali. —Kannan Subramanian R My contribution to this book would have not been possible without the support of Remya and Veda. Our experience with Apress has been wonderful. We thank the Apress team for their professionalism and a good, fine print. Dr K.K. Sudheesh

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Preface Banks are financial intermediaries. The four key intermediation functions, as stated by the Bank for International Settlements, are credit, maturity, liquidity, and collateral transformations. In playing the role of a financial intermediary, banks are exposed to market, credit, liquidity, and operational risks. Risk taking is an inseparable part of banking, and the impact of a risk can be more than the bank’s risk appetite for it, or in stressed situations, more than its risk capacity. Risk events can result in a direct loss or constrain the bank’s ability to seize business opportunities, or could lead to the collapse of the institution in a crisis situation. The definition of risk has changed over the last several decades. From being defined in terms of the probability of a loss occurring, and managing the impact of uncertainty in business, it has evolved into a forward-looking, enterprise risk-adjusted return management capability that helps a bank manage adverse internal and external events. Banks are finding it difficult to manage their businesses efficiently because they are constrained by their fragmented business and technical architectures. The fragmentation has created a complex banking operating environment that hinders business growth. Even as a new breed of tech-savvy, non-banking companies have emerged as competitors, large banks face the challenge of working with a knackered infrastructure that has burst at its seams. The new entrants are demonstrating their capability in leveraging technology and improving the customer experience. “Siloed risk management” refers to the risk-management approach in which the perceived dominating risk type of a financial contract drives the risk-mitigation process. For instance, risk management in treasury contracts is primarily focused on market risk, as the characteristics of the contract are sensitive to market rates and prices. However, while several contract types have a credit risk exposure as well, market and credit risks are not managed simultaneously, even if the risks are known. Some of the risks are managed in a downstream application at a different point in time. In the business of lending, credit risk is the dominant factor. For instance, some banks treat floating-rate loans as being without market risk. However, the probability of default could increase with a rise in interest rates, as the borrower’s obligation increases. Hence, the actual xxi

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probability of default is underestimated in some situations. In most banks, market and credit risks are managed without considering their impact on liquidity. Commercial banks rely on retail deposits to support their asset growth. Increased competition and unpredictable customer behavior have increased the rate sensitivity of retail deposits. Because of this, banks use alternative sources of funding, such as from the wholesale and “brokered” markets. However, these exposures carry more rate and liquidity sensitivity risks than do traditional retail deposits. Further, an increasing number of banks offer asset and liability products with embedded optionality, on both sides of the balance sheet. This has made cash-flow management more challenging, and the liquidity riskmanagement mechanism has not kept pace with these changes and their associated complexities. By taking an incremental approach to automating their growing business requirements, many banks are increasing the complexity of their operations. They are not resolving the known risks and limitations in their current operating environment. The weakness is predominantly in their enterprise architecture and data governance. Basel’s recent recommendations on the Fundamental Review of the Trading Book, Interest Rate Risk in the Banking Book, Intraday Liquidity Management, Liquidity Coverage Ratio, and Net Stable Funding Ratio have a significant impact on data and enterprise architecture. These requirements make it imperative for a bank to take a holistic, enterprise view of their risk-adjusted returns. It is an opportune moment for banks to transform their operations into agile enterprise models that will allow them to stay relevant and competitive. Enterprise data ontology is an approach that provides the banking industry and relevant stakeholders with a common understanding of the business terms. It provides a framework for conceptual enterprise risk-return modeling (ERRM) that leverages advanced analytics and knowledge management. The four important dimensions of ontology-based ERRM systems are metamodels, procedural knowledge, temporal relations, and knowledge acquisition. These components are consistent with the inherent nature of the quantitative modeling of risk-adjusted returns in commercial banks. The purpose of semantic technology is to uncover meaning within data, which is a pre-requisite for unlocking its value. Initiatives such as the Financial Industry Business Ontology (FIBO©) of the EDM Council Inc. are paving the way for the implementation of ontology-driven systems. In most banks, data has been a “by-product” of their product-oriented approach toward computerization in the “Electronic Data Processing (EDP) era.” This book puts forth the reasons why enterprise architecture and enterprise data governance should be xxii

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considered as pre-requisite capabilities for enterprise risk-adjusted return management. Banks are making a paradigm shift in their approach to managing their enterprise architecture so as to improve their return on investment in technology. This is driven by changes in the economy, business models, customer expectations, business delivery channels, and risk-management requirements. This book is for enterprise risk-adjusted return management in commercial banks and is for a global audience. Most banks buy their banking software solutions, and this book guides a bank in making the right choices and decisions. The first two chapters explain the “as-is” environment in commercial banks. The first chapter provides an overview of financial markets, banking products, the source systems, and the evolution of Basel risk-management recommendations. The source systems include the core banking system and the specialized solutions for treasury, loans, and trade finance. Small banks typically use a core banking system for most of their business requirements. Chapter 2 explains the risk-management solutions that exist in the as-is environment, from an implementation perspective, with a focus on data management. Chapter 3 differentiates the enterprise risk-adjusted return model from the loosely used term “integrated risk management” by explaining the scope of the enterprise approach. It provides guidance on the approach needed to conduct a gap analysis for identifying what needs to be done to transform the bank from the as-is, siloed, complex model by operationalizing the target enterprise risk-adjusted model. Presently, in the siloed architecture, data is extracted from disparate source systems at different points in time through a series of extract-transform-load processes and then uploaded into data warehouses for risk control and reporting purposes. The data flows between business functions have become increasingly convoluted, and it is an immense challenge to establish data lineage. The third chapter also covers the evolving changes brought in by what is being referred to as Basel IV requirements and provides an assessment of the impact of these changes on enterprise architecture and data management. Interoperability, flexibility, and scalability are primary drivers for the choice of a bank’s enterprise architecture. This book recommends a bottom-up approach to transformation because that is the only way to have a granular enterprise data model that can provide a single view of the truth. Chapters four to nine explain how a bank can improve and build the capability to implement an enterprise risk-adjusted return model. The Stanford Encyclopaedia of Philosophy refers to the Bayes Theorem as a simple mathematical formula. Encouraged by that usage of the word “simple,” this book recommends a simple methodology for xxiii

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implementing the enterprise risk-adjusted return model in Chapter 4. It provides a transformation plan for operationalizing the enterprise risk-adjusted model and explains all the milestones in it. The approach to accomplishing the milestones is explained from Chapter 5 to Chapter 10. Chapter 5 explains enterprise architecture, event-driven architecture, and enterprise business-process automation. Digital transformation is not just a technology initiative; it is an imperative that should be acted upon like a business opportunity for gaining a sustainable competitive advantage. The objective is to create a user-friendly, scalable, and flexible digital infrastructure that meets the challenges of demanding service-level agreements. Change management is a critical task during the transformation, and the management of a bank should demonstrate leadership in managing the change. As components become less harmonious, the degree of complexity increases. Although complex and simple systems are disjoint categories, it is possible for a bank to significantly reduce the complexity in its target operating model. It would be wrong to assume that enterprise architecture is the sum total of the underlying simple components. Therefore, it is necessary to deconstruct the enterprise into logical parts, such as services and data elements. A bank could choose a service-oriented architecture or a micro-services architecture approach and then build a loosely coupled, interoperable set of components that are consistent with enterprise architecture principles. An event can trigger a set of actions that can drive services, processes, and activities down a desired path for realizing a defined objective. An event-driven architecture is an ideal environment for risk management, as it facilitates the implementation of concepts such as state transitions and methodologies like system dynamics. The global initiative Banking Industry Architecture Network is recommending this direction. Process automation ensures straight-through processing. There is a lot in common between enterprise process taxonomy, enterprise data ontology, and enterprise data flow. A bank that has implemented process automation will find it easy to implement ontologybased enterprise risk management. This book advocates a process-based approach to managing the operations of a bank and explains the usage of the Business Process Management Suite for process automation. A process-based approach to managing operations will make it easier for a bank to monitor the different risk types in a single framework and manage them in real-time. Banks that have the capability to measure process maturity are more likely to successfully leverage robotic process automation. This chapter provides several examples of process automation and its benefits. xxiv

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Chapters 6 and 7 are on enterprise data management, with the latter focusing on risk data. Chapter 6 explains enterprise data ontology in the context of the source systems and their data entities. The Data Management Association DAMA’s Data Management Book of Knowledge-2 and the Data Management Capability Model (DCAM), created by the Enterprise Data Management Council (EDMC), form the basis for this chapter’s data management methodology recommendation. “Data semantics” refers to the understanding of each data item from a standard usage perspective. DCAM is an ontology-based methodology for the banking industry, and the DAMA model is industry agnostic. The changing technology landscape and the “architecture-sensitive changes” made by Basel to its risk-management recommendations make it necessary for the banking industry to focus on ontologydriven risk-adjusted return systems. The data-centric approach helps a bank to operationalize its enterprise risk governance. The focus of enterprise data management is not on data storage and visualization. Pretty dashboards and colorful infographics are only as good as the underlying data. The focus is on data flow, lineage, democratizing access, and having a single view of the truth. Data lineage is an important aspect of data strategy because it ensures accuracy, consistency, and transparency. Chapter 7 explains, with examples, enterprise data risk ontology by illustrating the taxonomies of market, credit, liquidity, and operational risks. Risk ontology comprises data characteristics, classifications, properties, relationships, and attributes. It improves risk identification, analysis, measurement, and mitigation. It facilitates causal analytics and supports the identification of countermeasures that can mitigate risk. Risk policies and metrics are embedded into business processes that are orchestrated in a loosely coupled service architecture that uses data as a service. Data scientists provide the energy for driving events and process orchestration. In the background of enterprise data management methodologies, the concepts of defensive and offensive uses of data in an event-driven architecture are explained. The benefits of using graph databases, time-series database (TSDB), and in-memory management are explained with examples. In the final selection, the narration provides examples of an enterprise multidimensional data model that is an output of the datacentric approach to designing the architecture. It provides guidance on using a data governance maturity model. A bank with a mature customer-centric business model can leverage a data-­centric enterprise architecture to gain a competitive advantage.

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Chapters 8 and 9 explain risk data science, analytics, and enterprise knowledge management. Banks should manage data as a science and view their technology division as data and service providers. This book is not on financial engineering and is not meant for quants. The references to quantitative techniques and risk modeling are from the perspective of the unique character of risk data and its governance. The focus is on how to build and use an enterprise risk-adjusted return model that is data-centric, services based, and event driven. This is not a book on software development or quantitative risk modeling. Chapter 8 explains data characteristics, the basics of risk mathematics and statistics, risk-management theories and concepts, popular risk models used by banks, and model governance. Enterprise data models cannot be created and used without having an understanding of the underlying data. Data taxonomy is an important input for riskmodel taxonomy, a critical success factor for risk-model governance. Chapter 9 focuses on advanced analytics and enterprise knowledge management. American sociologist Earl Robert Babbie said “[s]cientific inquiry in practice typically involves alternating between deduction and induction. Both methods involve interplay of logic and observation.” Data scientists and risk analysts are fascinated by quantitative models. Black swan events have exposed the limitations of risk models and established the need for wholesome data to manage extreme events. Given this background, the authors opine that enterprise data and knowledge management are critical success factors for managing tail behavior. Chapter 9 provides several examples for machine learning and knowledge management applications in banks. The enterprise data ontology framework should be the basis for creating and using the enterprise knowledge management database. It is interesting that many machine learning concepts that are being applied to risk management and business analytics can be traced back to previous centuries. Two hundred years ago, Leonhard Euler laid the foundation for the graph database and knowledge graph with his graph theory. The increasing use of advanced data analytics and cognitive technologies has emphasized the need for high-quality data. Risk prevention and proactive risk management require forward-­looking scenarios and predictive analytics. Given the high-severity risks associated with the “black box approach” to machine learning, countries have begun to regulate the design and usage of machine learning applications. For instance, in the context of artificial intelligence and machine learning, the United Kingdom’s Financial Stability Board has issued guidelines on issues such as data privacy and protection, xxvi

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consumer protection, anti-discrimination, and cross-border issues. Knowledge management is a specialized domain that can improve decision making by providing actionable intelligence. It is a multidisciplinary field and includes mathematics, cognitive science, and psychology. Graph databases enable banks to look beyond the individual data points and focus on the connections that link them. A knowledge graph is created when an ontologybased data model is applied to a set of individual data points. It contains meaning and facts about the enterprise risk-adjusted return ecosystem. Knowledge graphs provide semantic enrichment to content and help a bank leverage its capability to have a single view of the truth. Some international banks have created a knowledge management center of excellence. The primary objective of these centers is to leverage data so as to optimize risk-adjusted returns. These centers are supported by the chief data officer and their team of data-governance experts. Banks with high operating-model and data-governance maturity scores can aspire to be knowledge-driven enterprises by creating a culture of continuous improvement. Chapter 10, the last chapter, focuses on the significant benefits that a bank can derive from the recommended capabilities. “With market risk and credit risk, you could lose a fortune. With liquidity risk, you could lose the bank!” said Bruce McLean Forrest of the UBS Group Treasury. His statement implies that a liquidity risk could quickly transform into a solvency risk. The function of a bank’s treasury is seeing a dramatic transformation. Enterprise liquidity management, dynamic asset liability management, and balance sheet optimization have become critical functions of a real-time treasury. This chapter explains an approach to creating and using an enterprise liquidity hub, a capability that a bank could leverage to significantly improve these functions. In the years preceding the 2007–2008 financial crisis, many of the failed banks were reporting higher-than-average net interest margin (NIM). The risks in their balance sheets were not visible. When the crisis stress tested their loan books, the weakness of their asset quality became evident. The implication of being driven by NIM without risk adjustment is that the banks are likely to end up with risky loan portfolios. This book recommends that supervisors encourage banks to report their risk-adjusted NIM. Further, banks could also report liquidity-adjusted market risk and liquidityadjusted credit risk so that stakeholders get a better appreciation of the bank’s risk profile.

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There is a consensus that enterprise risk is not a simple summation of the risk types. Copula is one of the best techniques with which to integrate market, credit, and operational risks. Market risk is often modeled by a Gaussian distribution. Credit risk and operational risk are modeled with more skewed distributions due to occasional extreme losses. Banks can use copulas to obtain a single measure for assessing the enterprise risk: a single value that represents market, credit, and operational risk exposures. This can be accomplished by forming a joint distribution from specified marginal distributions in an internally consistent and realistic manner that preserves the characteristics of the individual risks. The selection of the copula function determines the quality of the output, and enterprise data governance facilitates the selection of the right type of copula. Bill Gates has stated, “[B]anking is necessary; banks are not.” Mistaking person-to-person payments as banking and creating an irrational exuberance over FinTech has not significantly improved the quality of bank balance sheets. There has been some progress in using new technologies like blockchain for payment and trade finance. However, there is no published information on the return of these investments. If a quarter of the effort invested in FinTech had found its way into improvements to the enterprise architecture, enterprise data management, and enterprise risk-adjusted return management, then the global banking industry would be better off in real terms. Banks should apply the Occam’s Razor principle to avoid creating a complex banking environment. The famous statistician John Wilder Tukey opined that mathematicians should start with their data and then look for a theorem, rather than vice versa. An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem. This book is guided by the “Tukey Principle” and uses a data-centric approach to explain enterprise risk-adjusted return management. We hope that the banking community benefits from reading this book.

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Commercial Banks, Banking Systems, and Basel Recommendations Commercial banks play a critical role as financial intermediaries that channel funds from savers to borrowers in an efficient manner. In doing so, banks are exposed to market, credit, liquidity, and operational risks. These risks should be proactively managed in a holistic manner with a focus on risk appetite and risk-adjusted returns. Managements of banks need an enterprise view of the risks for improving capital allocation decisions. During the last decade, with an objective of moving toward an enterprise risk-management model, banks have increased their investments in transforming their operating model. The banking aspects of this chapter, particularly sections 1.1 and 1.2, will be elementary for bankers. However, as the book focuses on enterprise data and enterprise risk-data ontology, it is important for professionals who are not banking experts to understand the banking business model, as it influences the choice of enterprise architecture and technology. The following is a brief description of the layout of this chapter: •

Section 1.1 provides an overview of the Financial Markets.

•

Section 1.2 explains the product portfolios of treasury, corporate and retail banking. Cash flows of products are illustrated, as enterprise liquidity management is a core component of the target enterprise risk-adjusted return model.

© Kannan Subramanian R and Dr. Sudheesh Kumar Kattumannil 2022 K. Subramanian R and S. K. Kattumannil, Event- and Data-Centric Enterprise Risk-Adjusted Return Management, https://doi.org/10.1007/978-1-4842-7440-8_1

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•

One of the reasons for the siloed architecture is the product-centric computerization implemented by banks. The customer-facing and accounting systems are explained in Section 1.3.

•

Section 1.4 provides an overview of the evolution of Basel risk-­ management recommendations. The section provides an overview of the Basel releases and explains some of the main recommendations in each release.

1.1  Financial Markets The term “financial market” refers to the currency, money, capital, and commodity markets. Banks are a part of this large, important ecosystem within the economy.

1.1.1  Currency Market (FX market, Forex market) The transactions in the currency market comprise (i) the interbank market, in which financial institutions trade currencies; and (ii) the retail over-the-counter market, in which individuals and businesses trade through online platforms and currency brokers. Forex trading is dominated by four currency pairs, known as “the majors”: •

USD/EUR (US dollar/euro)

•

USD/JPY (US dollar/yen)

•

USD/GBP (US dollar/British pound)

•

USD/CHF (US dollar/Swiss franc)

Forex transactions are processed in the continuous linked settlement (CLS) system. CLS is linked to the real-time gross settlement (RTGS) systems of central banks around the world to facilitate the simultaneous settlement of the different currency legs of forex transactions. This eliminates the Herstatt risk, which refers to a settlement situation wherein one currency fails while the other completes. Exchange rates are determined in the interbank market, and central banks participate in the FX market to stabilize their currency exchange rates by buying and selling currency in sufficient quantities.

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1.1.2  Money Market Money market instruments are short-term, interest-bearing assets with maturities of less than one year. They include treasury bills, commercial paper, and certificates of deposit. The main issuers of financial instruments in the market are the government, banks, and private companies. The main investors include banks, pension funds, and provident funds. The central bank plays a strategic role in the money market. The bank manages cash liquidity and interest rates via open market operations (OMOs), managing repo rates, stipulating reserve requirements, and by regulating access to its accommodation. A mature money market facilitates a smooth implementation of monetary policy.

1.1.3  Capital Market The capital market is the place for trading equity shares and financial instruments that have maturities of greater than one year, such as treasury bonds and private debt securities (bonds and debentures). The market has two interdependent and inseparable segments: the new issues, also known as the primary market, and the secondary market. The main objective of the capital market is to facilitate the raising of long-term funds. The three categories of participants are (i) the issuers of securities; (ii) the investors in securities; and (iii) the intermediaries.

1.1.4  Commodities Market The commodities market is the market for goods that are raw or partly refined. The commodity’s price reflects the cost of finding, gathering, or harvesting it. Examples include grains, livestock, food, metals, energy, and exotic commodities. Physical contracts are based on the sale of goods, which are delivered immediately, for cash. Derivative contracts are available for a range of physical commodities, market instruments, and indices.

1.1.5  Exchange and the Over-the-Counter (OTC) Market The exchange acts like a counterparty for each transaction and ensures completion of the settlement process. Members registered with the exchange should comply with the rules of the exchange. The exchange mechanism enables transaction enforcement and strict discipline. An exchange-traded product is a standardized financial instrument and has more liquidity than over-the-counter (OTC) instruments. 3

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Over-the-counter markets are decentralized, and the costs for intermediary services are relatively lower. There is a counterparty risk in the transactions, but the market is important as it allows participants to avail themselves of customized resolutions for gap exposures and the hedging of mismatches. The unique needs of different participants has led to the evolution and growth of OTC financial instruments. Electronic trading has contributed to the growth in the volume of instruments traded in OTC markets. Table 1-1 highlights the main differences between exchange-traded and over-the-­ counter contracts.

Table 1-1.  Comparison of Exchange and Over-the-Counter Contracts

S  ettlement The settlement processes, i.e., the pay-ins and pay-outs, are processed by the exchange’s settlement system. Contracts such as futures can have two types of settlement: the mark-­to-­market settlement and the final settlement. The former happens at the end of each day, and the latter happens on the last trading day of the futures contract. Brought forward, newly created or closed-out positions are marked to market, at the daily settlement price or the final settlement price, at the close of trading hours. If a listed entity has been traded, the daily settlement price is the consensus closing price arrived at after the closing session. If there has been no trade, the daily settlement price is computed as per the methods prescribed by the exchange. All open positions in a futures contract cease to exist after its expiration day. Financial futures are mostly cash settled, and settlement of commodity futures can be done by physical delivery or by closing out open positions.

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1.2  Commercial Bank — Lines of Business and Products The accounting methods of banking products and services is out of the scope of this book. However, it is important for a bank to (a) identify and maintain a record of all financial instruments offered by it; (b) classify the financial instruments, e.g., held for trading, held to maturity, available for sale; (c) recognize off–balance sheet transactions; (d) develop measures to assess risk; and (e) recognize revenue, gains, and losses in compliance with relevant regulations. The three lines of business (LOB) of a commercial bank are the treasury, corporate banking, and retail banking. Figure 1-1 provides an overview of the treasury ecosystem. This is the most dynamic business line of a commercial bank.

Figure 1-1.  The treasury ecosystem

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The lines of business are supported by the finance, operations, information technology, human resources, and legal departments. This section provides an overview of the lines of business (LOB) and their products.

Banking product/instrument cash flows are a fundamental aspect of risk management. These are governed by contractual agreements between the bank and the counterparty or customer. To emphasize the importance of cash flows and contractual terms in Enterprise Risk adjusted Return (ERR), section 1.2.1 provides examples of cash-flow profile and contract specifications.

1.2.1  Treasury — The Hub of the Bank The core functions of the treasury are to manage the liquidity of the bank and also to manage the treasury’s portfolio of products that can be classified under foreign exchange, money market, commodities, and equities. The treasury portfolio includes the derivative and non-derivative businesses.

1.2.1.1  Foreign Exchange The FX market is for making payments or transferring funds across borders. It is also the place where the exchange rates between different national currencies are determined. A commercial bank provides a range of products for individuals and business entities. An FX spot is a transaction executed at a price agreed to today, where one currency is used to buy another currency, with settlement in two business days. The rate is the spot rate. The value date is also the maturity date of the contract and is two working days hence (Figure 1-2).

Figure 1-2.  FX spot Banks offer the following products to their customers for managing foreign exchange risk. 6

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An FX forward contract is a transaction to buy one currency for another currency at a rate agreed to on the day of the transaction, with a settlement date of an agreed-upon future date. Outright forward rate = spot rate +/- forward points. Forward contracts are available in major trading currencies and are transacted on an OTC basis. The maturities vary and are categorized as either straight or broken dates. The former are contracts that are quoted for settlement in one, three, six, or twelve months. These are quoted widely. Other maturity periods are negotiated with the bank and are referred to as broken dates. An outright forward has no payment up front but is an obligation for a physical exchange of funds at a future date at an agreed-upon rate (Figure 1-3).

Figure 1-3.  Outright forward Table 1-2 illustrates the date on which FX deals are settled.

Table 1-2.  FX Deals and Value Date

Non-deliverable forwards (NDF) are used for hedging currency risk and do not involve an exchange of principal. They are settled against a fixed rate at maturity. Foreign currency options give the buyer the right but not the obligation to undertake the transaction, to buy or sell foreign currency, at an agreed-upon future date. See Table 1-3 to see the difference between options and forward contracts.

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Table 1-3.  Difference between Options and Forward Contract

Currency futures are quoted against the USD in sterling, yen, and euros. The instrument can be used for hedging. Currency futures are well suited for dynamic hedging. In this, a bank can set the price at the prevailing exchange rate and hedge the accumulated position when its exposure volume reaches a certain level. A currency swap is a simultaneous purchase and sale of one currency for another with two different value dates. The rate of exchange is settled up front between the bank and the customer. The principal amounts are exchanged between the bank and the customer. An FX swap consists of two legs: (i) a spot FX transaction (short leg) and (ii) a forward FX transaction (long leg). The two legs are executed simultaneously for the same quantity and offset each other. Forward points are based on interest rate differentials between the two currencies. In the forward market, the currency of a country with a lower interest rate than the domestic currency will trade at a “premium.” The currency of a country with higher interest rates than the domestic country will trade at a “discount.” The different types of currency swaps are fixed-to-fixed swap, floating-to-fixed swap, and floating-to-floating (basis) swap. Table 1-4 provides an insight into the data items in a currency swap contract.

Table 1-4.  Data Items in a Currency Swap

-

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Cost of Carry Cost of carry is measured using the interest rate of a risk-free asset. It is the cost of holding an asset for the contract period and is measured in terms of the difference between the actual future prices and the spot value of the underlying asset. The nuances can vary for the different financial instruments. In the case of commodities, it could include insurance, storage, and transport. Table 1-5 mentions the data items and its characteristics for four different FX contracts.

Table 1-5.  FX Contract-Data Attributes

-

1.2.1.2  Money Market The primary function of the money market (MMKT) is the borrowing and lending of funds for periods of a year or less. MMKT instruments that are actively traded in the market are classified as liquid assets. Fixed-income securities can be seen in Table 1-6.

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Table 1-6.  Fixed-Income Securities

Commercial banks •

borrow in the money market to fund their loan portfolio;

•

acquire funds to meet the central bank’s reserve requirements;

•

manage their funds in the MMKT on an overnight basis or on a short-­ term basis using the discount window or for longer periods using a certificate of deposit;

•

enter into repurchase agreements (repos) or reverse repos;

•

participate as dealers for OTC interest rate derivatives; and

•

provide financial commitments that ensure investors in money market securities are paid on a timely basis. This can be in the form of a (i) backup line of credit to issuers of money market securities or (ii) a guarantee for a redemption of a security at maturity.

Bonds Bonds are debts issued by a government or a corporation. The interest rate is fixed at the time of issue, and the interest is normally paid semi-annually. The issuer repays the bond value at maturity. There are different categories of bonds. The two popular 10

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ones are coupon bonds and zero-coupon bonds. Coupon bonds come with an interest rate for a defined maturity period. The price of the bond is determined based on the present value of the future cash flows. The bonds are traded at yield to maturity, and the prevailing rates of interest have a direct impact on the price of the bond. Zero-coupon bonds do not carry an interest rate. They are issued at a discount and repaid at face value on the maturity date.

Figure 1-4.  Bond cash-flow profile

Repurchase Agreement A repurchase agreement is the sale of a security, where the seller makes a simultaneous commitment to repurchase the security from the buyer at a future date and at a predetermined price. The securities can serve as collateral. The repo rate is the interest paid on the loan and is computed as the difference between the initial price and the repurchase price. In a reverse repurchase agreement, one party purchases securities and agrees to sell it back for a positive return at a later date. Repos may be executed as bilateral repos, whereby repo sellers and buyers transact directly with each other.

A Tri-party Repo In a tri-party repo, a clearing bank handles the settlement and operational issues. Most of the transactions have an overnight maturity. Table 1-7 highlights the main activities of the process. The custodial banks provide clearing services to the tri-party repo market and are responsible for settlement and pricing. They also facilitate collateral substitution.

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Table 1-7.  Tri-party Repo Process

The other money markets include treasury bill (T-Bill), treasury note (T-Note), treasury bond, government securities (G Sec), commercial bill, commercial paper, certificate of deposit, and bond futures. Interest rate options can be classified as (i) options in interest rate futures; (ii) options on Forward Rate Agreements (FRAs); or (iii) options on interest rate swaps (IRS), i.e., swaptions.

1.2.1.3  Equity An equity swap is an agreement based on a notional principal between the bank and its customer where one party pays the other a rate of return based on the stock market index. The other party makes a payment on another index or a fixed or floating rate. The payments are based on an agreed-upon percentage of the notional principal.

Options & Futures There are two categories of equity options: (i) options on individual equities or (ii) options on stock index. Futures are similar to options and can be held on an individual stock or on the index.

1.2.1.4  Commodity Commodity derivatives provide a diversification benefit because they have low correlations with equities and bonds. The categories include options, futures, and swaps.

Commodity Options & Futures Commodities futures are agreements to buy or sell a raw material at a specific date in the future, at a particular price. The contract is for a set amount. 12

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Commodity Swap In a commodity swap between a bank and the customer, one set of payments could be driven by the price of a commodity or the commodity index. The other set of payments could be based on a fixed or floating rate or a commodity price or index. Table 1-8 provides an example of a commodity futures contract’s specifications. The contract specifies, among other things, the quality of the deliverables and the possible delivery locations. The characteristics of commodity futures contracts vary and depend on the nature of the underlying.

Market Characteristics Each market has different seasonal characteristics. Each differs in carrying costs and has a different convenience yield, which is a measure of the gain resulting from holding the spot commodity. Carrying costs and convenience yields are highly uncertain and add a degree of difficulty in measuring the market risk of a portfolio of commodity futures using the spot commodity price as the market risk factor.

Table 1-8.  Contract Specifications (Data Attributes) — Commodity Futures

Post-trading Functions “Post-trading” refers to the activities that take place after an exchange of securities has been agreed upon between parties. Clearing and settlement infrastructure and processes are a critical part of the enterprise infrastructure. The post-trading processes are the transfer of ownership of the securities from the seller to the buyer and the payment. Efficient and safe post-trade infrastructure is a key element of a well-functioning financial market.

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Risks Associated with Derivatives The inherent characteristics of some derivatives make their cash flows non-linear, with the (non)linearity being determined by its payoff function. This is core to effective risk management. In these contracts, market risk can result from changes in the market rates of the underlying, and credit risk can arise as a result of the counterparty’s defaulting on its obligations under the contract. The use of derivatives could create a liquidity risk as there is an impact on a bank’s leveraged position. Derivatives are prone to different operational risks, including (a) documentation risk; (b) system and model risk; (c) legal risk; and (d) tax- and accounting-related issues. See Table 1-9.

Table 1-9.  Risks in Derivatives

The top part of Figure 1-5 illustrates a flow in an exchange-traded process, and the lower part of the figure illustrates the flow in an OTC market. The figure highlights the data dimensions of the contracts.

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Figure 1-5.  Risk data dimensions of derivatives

1.2.1.5  International Swaps and Derivatives Association (ISDA) The International Swaps and Derivatives Association (ISDA) has developed standard documentation for derivatives; its documentation architecture for a single contract covers the following: i. The Master Agreement1 (MA) is a comprehensive document that includes the standard provisions of a derivatives contract and can be varied by the schedule to the master agreement. ii. Schedule to the MA contains the amendments to the terms of the MA. The following provisions are often the subject matter of negotiations:

(a) Definition of specified transaction



(b) Cross-default

 OTC-Settlement Procedures & Counterparty Risk Management, https://www.bis.org/ publ/ecsc08.pdf, ISDA’s guidelines for Collateral Practitioners, https://www.isda.org/ book/2005-isda-collateral-guidelines/

1

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(c) Additional termination events



(d) Delivery of documents



(e) Netting1 iii. Credit support documents (CSD1) are the method of providing collateral for the obligations under derivative transactions. It could be an annex or a standalone support deed. The Credit Support Annex (CSA1) regulates collateral management under the ISDA Master Agreement. It contains the terms and conditions for using collateral as a credit risk mitigant. The scope of the CSA includes (a) how collateral calls and returns are to be calculated; (b) the mechanics and timing of transfers and valuations; (c) substitutions of collateral; (d) resolution of disputes; (e) default enforcement; (f ) default interest; (g) re-hypothecation; (h) allocation of expenses relating to the collateral arrangement; and (i) representations and warranties. iv. Confirmation contains the economic terms of an individual trade. This comprises the short-form confirmations, master confirmation agreement, and long-form confirmation. v. Definitions.

The ISDA Master Agreement has evolved from the 1992 ISDA Master Agreement to the 2002 ISDA Master Agreement, with the latter replacing earlier methods of calculating termination payments with the Close-Out Amount method. The ISDA Master Agreement includes both netting and close-out. “Pre-settlement credit risk” refers to the probability of a default. “Settlement risk” refers to the short-term risk in a cash flow or payment exchange. Hence (a) close-out netting is a mitigation for pre-­settlement credit risk and (b) payment netting is a mitigation for settlement risk. 1 In payment netting, two counterparties agree to net all payments in a single currency on a given value date. Netting is used to minimize risks in financial contracts and is managed by aggregating two or more obligations to achieve a reduced net obligation.

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Close-out allows the termination of all contracts between the insolvent and a solvent counterparty without waiting for the completion of bankruptcy proceedings. Close-out netting is a combination of close-out and netting methods. 1 Novation netting creates a new obligation for the net amount by creating a new transaction that gives rise to an obligation for the same value date and in the same currency as the existing obligation. The following are the main steps in close-out netting: (i) existing transactions are terminated; (ii) termination values are determined; and (iii) termination values are netted to arrive at a single net amount. See Table 1-10. 1

Table 1-10.  Settlement Risk and Netting

Collateralization7 is a credit-enhancement technique for managing the risks arising from derivative transactions. Additionally, contract-specific techniques include break clauses, direct credit support like collateral, indirect credit support such as letters of credit and guarantees, credit risk transfer mechanisms such as insurance, and credit derivatives. The Master Agreement and credit support documents are the primary means for collateralizing the trading exposure in the derivatives market. Participants in the FX market use the International Foreign Exchange Master Agreement or the International Currency Options Market Master Agreement or a variant, combined with proprietary margin agreements, to collateralize their FX trading exposure. In the repo markets, the PSA/ISMA Global Master Repurchase Agreement is used to collateralize trading. The other important ISDA-related matters are (a) the CDS Standard Model; (b) the ISDA Standard Initial Margin Model 2016; and (c) the ISDA Model Netting Act 2018. The Netting Act is designed to provide a template that can be used by jurisdictions considering legislation to ensure the enforceability of close-out netting. This netting document is important because many regulators allow close-out netting for reducing risk exposure, and it is the single most important risk mitigation tool in derivatives markets, as it can significantly lower credit exposures between counterparties. The ISDA documentation provides a high degree of legal certainty over the enforceability of closeout netting. 17

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Treasury Summarized Balance Sheet, P&L Table 1-11 provides an overview of the treasury’s profit and loss (P&L) statement and its assets and liabilities. The balance sheet composition is very different from those for retail and corporate banking.

Table 1-11.  Summarized Template — Treasury P&L, Balance Sheet

The focus areas in treasury management are (a) funding techniques; (b) liquidity management; (c) off–balance sheet item management; and (d) balance sheet optimization techniques.

1.2.2  Corporate Banking Lending is a core business activity for commercial banks, and the loan book is a major source of revenue and risk. Effective credit risk management is one of most important aspects of a bank’s effort to improve its risk-adjusted returns.

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1.2.2.1  Loans — Commercial Lending “Commercial lending” refers to products and services specifically designed for businesses. The core set of corporate banking products and their characteristics are as follows: •

Overdrafts can be secured or unsecured. The borrower can overdraw funds beyond the available balance up to an agreed-on limit. Interest is payable only on the money used for the period of withdrawal compounded daily.

•

Working-capital loans are of a short tenor; i.e., less than one year.

•

Medium- and long-term loans are larger than working-capital arrangements and have a more elaborate approach for appraisal and monitoring.

•

Syndicated loans are provided by a consortium of banks, with one of them playing the role of the leader of the consortium. The rest are considered participant banks.

1.2.2.2  Small & Medium Enterprise Sector Small and medium enterprises (SMEs) are classified using parameters such as turnover and number of employees. Over the last couple of decades, progressive political leaders in several countries have implemented measures to boost credit for this sector. The lending and risk management processes of SMEs are different from the processes used for large corporations.

1.2.2.3  Specialized Lending Specialized lending would include the following: •

Project finance - This refers to the loans given for a large, complex, specialized industry; e.g., renewable energy, telecommunications

•

Object finance – Luxury aircrafts, cruise ships

•

Commodity finance – Mostly short-term lending

•

Real estate, commercial, and residential

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1.2.2.4  Trade Finance Trade Finance products can be grouped under Funded and Non-funded categories.

Funded & Non-Funded Trade Finance Facilities Funded facilities include cash credit, packing credit advance, post-shipment finance, bills discounting, export credit refinancing, bankers’ acceptance, loan against trust receipt, factoring, and bill/invoice forfeiting. Non-funded facilities involve the following: •

Letter of credit (LC), also called documentary credit, can involve variants because of different trade financing practices. The different types of LCs include export LC, import LC, Red Clause credit, revocable, irrevocable, confirmed, unconfirmed, standby letter of credit, revolving, and sight.

•

Bank payment obligation is an instrument that can reduce the complexity associated with LCs. It is an irrevocable undertaking given by a bank to another bank that payment of a given amount, at a specified time, will be made after a successful electronic matching of data is done in accordance with International Chamber of Commerce (ICC) Unified Rules for Bank Payment Obligation.

•

Bank guarantees can be either financial or performance: •

Financial guarantees are used to secure a financial commitment such as a loan.

•

When the guarantor or the issuing bank guarantees the ability of the applicant to perform a contract to the beneficiary’s satisfaction, it is a performance guarantee. A shipping guarantee is a type of performance guarantee.

Standard settlement methods include free on board (FOB); free alongside (FAS); cost, insurance, freight (CIF), ex-doc, in-warehouse, and ex-warehouse. Lending is a documentation-intensive process. The information submitted by the prospective borrower includes details about current and historical audited financial statements, including cash-flow projections. The following are the categories of credit reports that a bank prepares using the application details: (i) financial analysis, including repayment capacity; (ii) collateral identification and valuation; 20

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(iii) peer-group comparisons; (iv) guarantor support and related financial information; (v) loan terms, including tenor and repayment structure; (vi) pricing information, including relationship profitability data; (vii) covenants and requirements for future submission of financial data; (viii) exceptions to policy and underwriting guidelines; (ix) risk mitigants; and (x) credit rating. Table 1-12 provides an example for the P&L and balance sheet of a corporate banking division.

Table 1-12.  Summarized Template — Corporate Banking P&L, Balance Sheet

1.2.3  Retail Banking Retail banking provides financial products and services to the mass market. In the last few years, this line of business has faced a lot of competition from non-banking companies. It is a high-volume, low-margin risk-reward model. From the information in Tables 1-12 and 1-13, it can be seen that the balance sheet items of corporate and retail banking appear to be the same. However, corporate banking products and customers have a different profile. For instance, corporate loan exposures have a higher probability of default.

Table 1-13.  Summarized Template — Retail Banking P&L, Balance Sheet

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1.2.3.1  Retail Liabilities Savings, Current Account credit balances and Time Deposits are borrowings from customers.

Savings, Current Account, Time Deposits The core set of retail products are as follows: (a) savings account – have no maturity date and the funds can be withdrawn anytime; (b) current accounts – are call funds–based overdraft arrangements that reprice overnight; and (c) term deposits, which can have different maturities and characteristics. The key characteristics that affect cash flows are the breakage of deposits prior to maturity or availing loans using the deposit as a collateral (Figure 1-6).

Figure 1-6.  Term deposit cash-flow profile Deposit Insurance Depositors’ funds are placed at risk, and banks could create risky assets from these liabilities. Bank managers could be engaged in excessive risk-taking without the knowledge of depositors. This is an example of the moral-hazard problem. Deposit insurance guarantees the return of deposits to depositors, with a cap on the amount and without interest income, in the event of a bank’s liquidation. This insurance is normally provided by a government agency.

Safe Custody Service Banks provide safe deposit boxes to keep small valuables in their room vault. In many countries, customers have to place a term deposit to avail themselves of the service. The relationship for this service is that of a bailor–bailee. 22

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1.2.3.2  Retail Assets Banks that focus on retail banking have a large variety of financial products for the mass market.

Retail Loans •

Home loans are an important source of revenue for most retail banks. The loan is secured by taking a mortgage on the property.

•

Other retail loans – Banks offer a variety of loans to the mass market. The nature of the loan could vary depending on different criteria, including the customer type, the interest computation method (fixed/floating/hybrid/Rule of 78), the collateral (secured fully/partly, unsecured), the drawdown schedule, and the repayment schedule. In some cases, the government could provide refinancing schemes to fund the disbursement of the loans. The loan portfolio includes vehicle loan, educational loan, and purchase of consumer durables or lifestyle products.

•

The Rule of 78 is a financing method that was outlawed in 1992 in the United States for loans longer than 61 months.

•

Home improvement loans may not require a mortgage as the collateral.

•

Equal monthly installments comprise both principal and interest components and are common for personal loans and mortgages.

•

The annual percentage rate (or APR) is the amount of interest on the total loan amount. It is paid annually (averaged over the full term of the loan). A lower APR can translate into lower monthly payments.

•

In loans against equity shares, the bank provides an overdraft facility that is a prescribed percentage (e.g., 70%) of the value of an approved list of shares pledged as collateral with the bank. The loan exposures are marked to the market for daily movements in share prices. Banks demand more collateral or cash when the margins drop below a specified level (e.g., 30%).

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1.2.3.3  Private Banking/Wealth Management “Private banking” refers to a suite of products and services offered by banks to highnet-­worth individuals. Each country has its own threshold, and many banks target the top 20% of the “wealth hierarchy” in the working population for this business. Emerging markets have been the most attractive for wealth management products. The business requires staff with adequate knowledge and experience in currency management, securities trading, real estate, art, and bullion. The services include expert guidance on inheritance, tax planning, and retirement planning. The business requirements tend to be more customer-centric and tend to have customer- or segment-specific data and architectural requirements. Table 1-14 illustrates the different risk types at play for different retail and corporate banking exposures. Product risks are covered in Chapter 7, Section 7.4.1.1.

Table 1-14.  Risk in Corporate Banking Products

Note: C.R., M.R., ALM, and O.R. stand for Credit Risk, Market Risk, Asset Liability Management, and Operational Risk.

Business Delivery and Electronic Channels Branch Banking This is the traditional approach for customers to avail themselves of banking services. As personal interaction is one of the best approaches for building a customer relationship, the branch network continues to be a critical success factor for commercial banks, notwithstanding the successful adoption of e-channels. 24

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e-Channels E-Channels comprise internet banking, mobile banking, corporate desktop banking (e.g., trade finance functions), ATMs, kiosks, telephone banking, and debit and credit cards. The bank could choose to have its own ATM network, share an ATM network with other banks, or have a vendor provide the ATM service. The smart card has become versatile and is enabled to work at any point-of-sale terminal. Mobile payments are increasing, and many countries are implementing a less-cash-payment infrastructure. These channels are examples for the successful adoption of modern technology by banks to widen customer reach, provide better service, and create value for the customer.

1.2.4  Term Structure of Interest Rates (TSIR) In the business of banking, the “raw material” (input) and the “finished good” (output) are both cash. The time value of money is quantified by interest rates. The core income sources of a commercial bank are interest and fees. The treasury’s income includes trading profits. This is the business perspective of the preceding information about banking products. Most accounting and risk management functionalities are influenced by the behavior of interest rates. “TSIR” refers to the relationship between the yields and maturities of a set of bonds with the same credit rating. As longer-maturity bonds are riskier, their yields are higher than the yields of shorter-maturity bonds. The yield curve is a graph that depicts the relationship between yield-to-maturity (y-axis) and time to maturity (x-axis). Any group of fixed-rate securities that are of the same asset class and share the same credit quality can be plotted on a yield curve. The slope of the yield curve provides analysts with a perspective of the country’s economic health. The slope and shape of the curve reflect expectations about future interest rates and anticipated economic growth. Figure 1-7 illustrates the different types of slopes of a yield curve.

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Figure 1-7.  Term structure slopes An upward-slopping curve reflects higher rewards for staying invested in the security. It also indicates that investors expect the economy to move at a normal pace, with no significant downturns, such as a recession or higher inflation. An inverted curve can occur when long-term bondholders accept lower returns relative to short-term investors. In this scenario, long-term investors expect the interest rates to fall. The low interest rates are an indication that there will be a slowdown in the economy, which is indicated by the curve’s becoming inverted. When the curve is inverted for a relatively long period, then it could be an indicator of a forthcoming recession. A curve can go through a phase where the short-term interest rate moves closer to the long-term interest rates prior to becoming inverted. The small elevation in the middle has led the curve to be known as a humped curve. Humped curves could indicate an economic slowdown and low interest rates. A flat yield curve reveals that investors believe the central bank will cut interest rates. Therefore, a flat yield curve is often a sign of an economic slowdown. In the non-parallel yield curve, shifts in the yield curve do not change by the same number of basis points for every maturity. When executing interest rate scenarios, risk managers configure non-parallel shifts in a manner similar to deterministic rate scenarios. The following types of interest rate risks have an impact on enterprise liquidity management: •

26

Repricing risk refers to the probability that assets and liabilities can reprice at different times or amounts. This can negatively impact a bank’s earnings and capital. The known sources of risk are the time difference in the maturity of fixed-rate products and the changes to the interest rate for floating-rate products.

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•

Basis risk can arise when there is a change in the relationship between the yield curves of long and short positions with the same maturity, in different financial instruments.

•

Yield curve risk reflects exposure to unanticipated changes in the shape or slope of the yield curve.

•

Option risk is the risk arising from options embedded in the bank’s assets, liabilities, and off–balance sheet items.

The preceding risks are explained with illustrations in the context of net interest income and net interest margin in Chapter 2, Section 2.3.5, under “Asset Liability Management.” Enterprise liquidity management is explained in Chapter 10, Section 10.1.

1.3  Source Systems I ntroduction All accounting and customer-facing applications can be referred to as source systems. These are the systems from which data is extracted for middle-office functions, such as risk management, and back-office reconciliation systems. Many banks have separate solutions for treasury, trade finance, and lending (commercial and retail). Some have a separate general ledger system that is then integrated with other “books of account” contained in other systems. If specialized solutions are not used, then the treasury, trade finance, lending, and general ledger modules of the core banking system are used. This is the environment in small banks. Figure 1-8 provides a conceptual view of the business architecture of a commercial bank. The contracts and transactions processed in these systems are linked to the products and instruments explained in Section 1.2.

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Figure 1-8.  As-is business view of a commercial bank Risk management systems for market risk management, credit risk management, asset liability management (ALM), operational risk management, anti-money laundering (AML), and fraud management rely on data from these systems and are explained in the next chapter. “Siloed risk management” refers to the limitations from an enterprise risk-­adjusted return perspective. The limitations include lack of wholesome data, data-quality impairment, risk measurement (applicable risks) and mitigation done at different points in time for the same underlying exposure, and the impact of poorquality data on risk modeling. The work on quantitative measures for enterprise risk is still evolving. GRC stands for governance, risk, and compliance. The risk management features in core banking and lending systems (source systems) are limited. In contrast, driven by business requirements, vendors of treasury management systems have continuously upgraded their solutions with risk management features.

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The siloed business view in Figure 1-8 could be compared with the target business architecture illustrated in Figure 3-16 in Chapter 3.

1.3.1  Specialized Systems Many banks use specialized solutions for treasury, lending, and trade finance, as they find the core banking features insufficient for their requirements.

1.3.1.1  Treasury A treasury management system (TMS) has the following components: A. Market data B. Front-, middle-, and back-office functions C. Modules and their main features

(i). FX



(ii). Money market



(iii). Equity



(iv). Commodity



(v). Greeks



(vi). Hedging with derivatives



(vii). Interest rate risk management products



(viii). Derivatives trading

Market Data The real-time interface for market data covers the full range of treasury asset classes and instruments. Table 1-15 provides an understanding of the data imported from financial data providers.

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Table 1-15.  Treasury Front Office – Market Data Sources Market Data

Reuters

Bloomberg

Markit

Telerate

Currency spot rate

Yes

Yes

Yes

Yes

Currency forward rate

Yes

Securities

Yes

Yield curve

Yes

Yes Yes Yes

Yes

Exchanged trades futures

Yes

Exchanged trades options

Yes

Issuer credit rating

Yes

The data in the table is an example. A bank can choose to source data from any financial data provider.

Treasury Management System (TMS) Figure 1-9 illustrates the primary modules of a TMS, the front-, middle-, and back-office functions, and its integration with external data providers and internal systems, such as risk management and trade finance.

Figure 1-9.  Overview of treasury management system 30

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Instrument Coverage across Modules The minimum instrument coverage includes foreign exchange spot and forwards, deposits, repos, zero- and fixed-coupon bonds, floaters, forward rate agreements, futures, options, exotics, and index-linked derivatives.

Front, Middle, and Back Office The front-office component includes deal capture and processing. It provides the traders with pre-deal analytics, credit lines, and limit checking (see Table 1-16). This includes providing trading desks with incremental and marginal Value at Risk (VaR).

Table 1-16.  Treasury Management – Front-, Middle- & Back-Office Functions Funconal Treasury Area Market Data Front Office

Dealing; Posion management Instrument Pricing

Middle Office (Note: there are overlapping features in the Treasury Management and Market Risk Management systems – Chapter 2, secon 2.1 provides more details).

Pre-deal analycs Collateral Management P&L monitoring.

Back Office

Value at Risk limits can be set at currency leve l, desk level and porolio level. Collateral Management Confirmaon matching Nostro management Selement Accounng

Applicaon & Interface Interface with Telerate, Markit, Reuters, Bloomberg e.g. Reuters Dealing system in-house pricing libraries, vendor provided algorithms Risk Management is either (a) a TMS feature (b) or provided by a Risk Management System such as Market Risk Management via an interface.

. Interface with clearing and selement systems

For the middle and back offices, there is an overlap in functionality between the TMS and the risk management systems used by the treasury). Most treasury systems provide yield-curve and price-volatility analysis to manage interest rate exposure. Other middle-­ office functions include deal analytics, pricing, hedge effectiveness, calculation of greeks (sensitivities), VaR limit monitoring, and collateral management.

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A variety of middle- and back-office tools support the management of the trading and banking books. Position keeping, P&L blotters, rollovers, and reporting are provided to the front office by a seamless integration with middle- and back-office functions. Reports such as aggregated positions, P&L by contract, trader/location or combinations thereof are provided. The system auto-generates the settlement instructions, and settlement is done by asset classes. Partial settlement and carrying forward any unsettled portion are also important back-office activities. Nostro account reconciliation can be a standalone system, part of the core banking system, or part of the treasury system. Centralized collateral management ontology is explained in Chapter 7, Section 7.1.5.3.1.

The Modules The Key Features of the FX Module In addition to deals capture, verification, and deal ticket printing, the following are the features of the FX module: •

Limit-order warnings, forward risk breakdown, swap points calculations, position and credit-limit monitoring, and real-time P&L analysis are included.

•

Risks can be managed at various levels, including currency, currency pair, or aggregated, for a group of correlated currency pairs or a portfolio.

•

FX cashflow monitoring provides the capability to measure the net position for each currency and focus on the time buckets of the flows. For sensitivity analysis, a bank can use historical exchange-rate movements or random forward-looking scenarios.

Exchange Position and Cash Position The cash position for a foreign currency is reflected in the balance outstanding in the bank’s nostro account abroad. Exchange position is the balance of the aggregate purchases and sales made by the bank in a particular currency. Contracts that have a firm rate with the counterparty are included in the exchange position. In the case of

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forward contracts, the amount will be included in the position upon maturity. This overall position of the bank (surplus or deficit) in a particular currency is managed through cover transactions.

The Key Features of the MMKT Module The instrument coverage will include deposits, loans, and repos. The fixed-income securities will include zero-coupon and fixed-coupon bonds, floaters, and forward rate agreements. A bank builds up a portfolio over time and examines the net positions at different time periods using one of two approaches: (i) maturity ladder approach, which is based on the residual maturity of the instruments; or (ii) duration ladder approach, which is referenced to the original maturity of the securities. Figures 1-10a and 1-10b illustrate (a) a consolidated portfolio structure across countries; and (b) a domestic bond portfolio structure and a merged bond’s positions across portfolios.

Figure 1-10a.  Example of a portfolio structure (bonds)

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Figure 1-10b.  Example of a domestic bond portfolio structure Spreads Nominal spread is the spread over the treasury par curve of the same maturity. Z-spread (zero-volatility spread) is the spread that is added to every point on the treasury spot curve. The price of the risky bond is taken as a given, and the spread over the spot curve (Z-spread) is used to discount the bond so as to arrive at the risky bond’s price. OAS (option-adjusted spread) removes that spread for embedded optionality and compensates the buyer only for credit and liquidity risks. OAS + Option Price (bps) = Z-spread. Prices of debt securities are calculated at market prices. Dirty price = Clean price + Accrued interest. Figure 1-11 shows that for high-grade investment bonds, the spreads tend to increase with time to maturity, while for lower grades, e.g., grade CC, the spread tends to be wider at the short end of the curve than at the long end.

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Figure 1-11.  Term structure of discount bonds, yield curve, and credit spread For high–credit quality bonds, the spread curve is either upward sloping or hump-­ shaped, while for low–credit quality bonds the spread curve is downward sloping. The swap spread is the difference between the yield-to-maturity of the bond and the interest rate given by a straight-line interpolation of the swap curve. Specific risk is the risk of adverse price movements caused by factors related to the security’s issuer, whereas general market risk is the risk arising from movements in the general level of market rates and prices. The curve effect is the part of the return that can be attributed to changes in the yield curve. This effect can be further split into four different subcomponents. shift effect

parallel shifts in the yield curve

steepness effect

steepness of the yield curve

curvature effect

curvature of the yield curve

residual

any other factor causing a change to the yield curve

The first three effects explain the majority of the variance of the yield curve; the residual is usually very small. Models are calibrated periodically to obtain the best fit of the movements of the yield curve. The curve effects are based on a singular value decomposition of changes to the yield curve. This ensures that the total effect from the curve is equal to the sum of the four subcomponents.

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Curves are an important aspect of risk management. This is further explained in Chapter 2, Sections 2.1.1.2b and 2.1.2.2b. It is also explained under “Risk Data Science Management” in Chapters 7 and 8.

Duration & Convexity Duration is a measure of the effective maturity of a security and incorporates the timing and size of a security’s cash flows. It measures the price sensitivity of a security to changes in interest rates and is a method by which to compare interest rate risk between securities with different coupons and maturities. This is illustrated in Figure 1-12, which compares two bonds. Duration is a single number value that provides the measurement of the yield sensitivity of a bond. The following are properties of duration that influence investment decisions: •

As maturity increases, the duration also increases.

•

As yield level increases, the duration decreases.

•

If you lower the coupon payment, the duration gets higher.

•

Duration decreases as frequency increases.

Figure 1-12.  Comparing the durations of two bonds Modified Duration, Macaulay Duration, Key Rate Duration, Effective Duration •

36

The modified duration is measured as the percentage change in price per 1 percentage point change in yield. Modified duration is useful for determining how the price of a security or a portfolio of securities will change when the yield curve undergoes a parallel shift.

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The first-­order yield sensitivity of a bond is called the modified duration. The approximation can be improved by using a second approximation. This approximation is referred to as the convexity. The convexity measure of a security can be used to approximate the change in price that duration cannot explain. •

Macaulay duration is applicable to instruments with fixed cash flows and is a measure of time or maturity and is measured in years. This is in contrast to modified duration, which is a rate of change of price with regards to yield and is measured as percentage per unit change in yield.

•

Key rate duration is used when the yield curve changes in a manner other than a parallel shift (e.g., flattening or steepening). In key rate duration, the ±Δy is not added to the entire par curve. It is added only to the Yield to Maturity (YTM) at a specific maturity on the par curve, leaving the YTM at all other maturities unchanged. The specific maturity is called the key rate. Key rate duration is a vector representing the price sensitivity of a security to each key rate change. The sum of the key rate durations is identical to the effective duration.

•

Effective duration is a measure in which yield changes can change the expected cash flow (e.g., callable bonds). This is used for instruments with embedded options.

Sensitivity Measurement – DV01, PV01, IE01 •

DV01 provides the basic measures for evaluating the sensitivity or risk of fixed-income instruments. The DV01 is the derivative of price with respect to yield.

•

PV01 is a measure of sensitivity to a 1bp (one basis point) change in interest rates. The higher the PV01, the greater is the sensitivity to a change in interest rates. The implementation of a portfolio immunization policy would help achieve a zero active PV01. This metric is used by risk managers to measure the completeness of immunization. It is a useful metric for finding out the point at which rebalancing assets and hedges is required. 37

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Commercial Banks, Banking Systems, and Basel Recommendations

IE01 is a measure of sensitivity to a 1bp change in inflation. The measure could be positive or negative, reflecting the percentage change in the net asset position of a bank for a given rise or fall in inflation. The higher the IE01, the greater the sensitivity to a change in inflation. Like the PV01, banks’ immunization strategy can help achieve a zero active IE01.

Duration Hedge Ratio The duration-based hedge ratio minimizes the net price change in the value of the bond. The hedge ratio uses the durations of the cash and futures instruments to determine their relative volatilities. The following are the components of the ratio: •

The Macaulay durations of the cash and futures instruments

•

The prices of the cash and futures instruments

•

The yields to maturity associated with the cash and futures instruments

Convexity Duration is supplemented with a measure that captures the curvature of the price–yield curve. The convexity measure is the quantification of the sensitivity of bond price to interest rate changes. Figure 1-13 provides the price–yield schematic for an option-free bond. For a small change in yield, whether an increase or decrease, the percentage change in price is the same. For large changes in yield, the percentage change in price is not the same for an equal increase or decrease in yield.

Figure 1-13.  Price–yield relationship 38

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The graph on the right in Figure 1-13 consists of a curved line and a straight line that is a tangent to the curve. Convexity causes a pricing error. The error due to convexity can be compensated for by measuring the convexity and including it in the %-change in the price. When yield changes are large, a bond with a greater convexity is more attractive and will price higher. Negative convexity refers to the pricing behavior of callable securities when prices exceed the redemption price.

Equity Module Equity derivative trading includes options and futures, exchange-traded fund (ETFs), equity swaps, structured warrants, equity-linked structured products, fund-linked structured products, and volatility products. Treasury systems support mark-to-market of positions and VaR at the portfolio and trader levels.

Commodity Module This includes commodity swaps, forwards, and options. Deals are captured, valued, scheduled, invoiced, confirmed, hedged, and analyzed. Traders are provided with P&L reports to determine the effect of new deals on an overall portfolio. The treasury system supports marking to market and VaR calculation. The system has interfaces with commodity exchanges such as the Chicago Mercantile Exchange (CME). The CME’s SPAN methodology measures the maximum loss that a portfolio of physical instruments (including derivatives) can incur at a stated confidence level over a specified time period. It uses different P&L scenarios for a combination of risk factors that include price, calendar spreads, volatility, delivery risk, and time. The method aggregates all futures and options that have the same underlying as a portfolio. CME calls this combination a risk array. Most treasury systems support a range of hedging strategies and provide pricing models such as Black Scholes, Garman-Kohlhagen, and Cox-Ross-Rubinstein.

Greeks and Risk Sensitivity The Greeks measure the change in the value of options and are linked to key risk parameters. The sensitivities allow traders and risk managers to manage the market changes that impact the value of the derivative positions. 39

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The first- and second-order sensitivities with respect to the underlying price are denoted by delta and gamma. Sensitivities with respect to volatility are not denoted by Greek letters. Vega and volga are the first two partial derivatives with respect to volatility, and the cross-price–volatility derivative is called Vanna. Delta is the sensitivity of the option value to a change in the price of the underlying, and gamma is the option price sensitivity to a change in delta. Gamma is highest when the option is at the money and increases as the option approaches expiry. For options near expiry and at the money, gamma will become more pronounced. These factors are important to note so as to keep a portfolio delta neutral. Theta is the option-price sensitivity to changes in time to expiry. As time passes, options becomes less valuable. This erosion of option value as time passes is known as time decay. Volatility is what is traded, and its level is determined by market forces. Implied volatility is “implied” in the price of the option. When the trading rooms make references to premium levels, they are referring to the implied volatility. The market-implied volatility of a standard European option, or just the implied volatility for short, is the volatility of the underlying that gives the market price of the option when used in the Black–Scholes–Merton (BSM) formula. A high vega in absolute terms indicates that the option is very sensitive to changes in volatility. Rho is defined as option-price sensitivity to changes in the risk-free rate. Here are some examples of limit setting based on Greeks:

40

•

Outright currency risk position limit is used for the spot FX trading business.

•

Interest rate delta limit is set for each maturity bucket in each currency. It is the dollar sensitivity of a portfolio to a one-basis-point shift in interest rates.

•

The delta-gamma limit is the risk appetite for options and is a control over specified movements in underlying rates. The limit uses the non-­linearity or convexity risk (gamma) inherent in open option positions.

•

The vega limit specifies the maximum loss an options book can sustain for a 1% shift in the underlying implied volatility rate, a key input into option pricing; e.g., from 12% to 13%.

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Hedging with Derivatives Interest Rate Risk Management Products: Forward Rate Agreement (FRA) This is an OTC instrument and is an off–balance sheet instrument. If the rate moves against the bank, then the bank pays the customer the difference between the reference rate and the FRA rate, based on the notional principal (Figure 1-14). If the rate moves against the customer, then the customer pays the bank. The rates are quoted in terms of the time to start and the time to end the notional loan period. A three-month loan starting in three months’ time is referred to as 3-on-6. FRAs are available in major trading currencies and are generally offered for up to two years. Banks prescribe the minimum notional principal. FRAs are generally available in intervals of three months.

Figure 1-14.  Constant term FRA cashflow profile Basic Interest Rate Relationships Interest rates in the market are affected by the risk-free real rate, inflation risk, default (credit) risk, term (maturity) risk, marketability (liquidity) risk, call risk, tax exemption, and other characteristics of the issue. As interest rates increase (decrease), value decreases (increases). For a given change in interest rates, as maturity increases, the change in value increases. Interest Rate Caps, Floors, and Collar Figure 1-15 illustrates interest rate risk management using caps, floors and collars. The cap contract sets the maximum interest rate, and the writer of the cap agrees to compensate the buyer if the interest rate rises higher than the strike level. There is no exchange of principal. The floor provides a guaranteed minimum rate for lenders or investors. Collar involves the simultaneous purchase of a cap agreement and sale of a floor. 41

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Figure 1-15.  Interest Rate Risk Management Interest Rate Swap (IRS) An interest rate swap (IRS) contract specifies (a) swap rate; (b) interest rate on which the floating rate is pegged; (c) the maturity date; (d) payment frequency; and (e) the notional principal. Payments between the parties are determined based on specified interest rates and a notional principal (Figure 1-16).

Figure 1-16.  Term swap cashflow profile Hedging ontology is explained in Chapter 7, Section 7.1.5.3.1, which it covers delta and dynamic hedging. Chapter-5 section 5.6.3.4 covers hedging from a process automation perspective.

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Valuation of an Interest Rate Swap The value of a swap at any date is equal to the net difference between the expected present values of the remaining fixed- and floating-rate payments. Commercial banks use interest-rate swaps in the following ways: •

Small banks can examine the feasibility of using a “macro-hedge” for hedging their net interest income against interest rate fluctuations.

•

Interest rate swaps can reduce funding costs. For instance, banks can choose to issue variable-rate borrowing instead of fixed-rate debt and hedge the risk by simultaneously executing a fixed-to-floating interest rate swap.

The interest rate risks explained in Section 1.2.4 are relevant for IR swaps. Further, the following are important aspects of a derivative contract: •

Price risk is distinct from the underlying.

•

Basis risk arises when the changes in the indices used for the derivative contract and the underlying loan move inconsistently.

•

There can be an opportunity cost if a fixed-rate investment turns out to be unfavorable when compared to the average of the floating rate over the same period.

•

Termination cost should be considered in determining risk–return strategies.

•

Settlement risk exists when the counterparty fails to make the required payments.

•

Tax and accounting issues can create regulatory and legal risks.

Long-term interest rate futures are used for long-term debt instruments. Table 1-17 provides a basic set of data attributes for interest rate risk management product contracts. These data elements have an impact on the risk exposure and the management of risks associated with the products.

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Table 1-17.  IRR Management Products, Contracts – Data Attributes

The pricing of interest rate derivatives requires the whole term structure of interest rates to be modeled. Most treasury management systems include the following models: (a) short-term rate as a function of one or more factors, i.e., Cox-Ingersoll-Ross or HullWhite or Black-Derman-Toy models; or (b) instantaneous forward rates (Heath-JarrowMorton); or (c) market-observable forward rates (e.g., the LIBOR market model of BraceGatarek-Musiela).

Derivatives Trading Table 1-18 provides a data-based view of derivatives trading strategies. Data elements such as interest rate movement can be configured to influence risk events affecting the “outlook attribute.”

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Table 1-18.  Derivatives Trading Strategies – Examples

Table 1-19 shows data collated from blotters and provides an overview of dealer– productwise open exposures.

Table 1-19.  Aggregated Instrument Data by Dealer

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Risk Attribution Analysis Most treasury systems support risk attribution analysis, a quantification process that decomposes the total risk of the portfolio into its individual risk units. The overlap between the risk management functionality in the treasury management system (TMS) and the market risk management (MRM) system (explained in the next chapter, Section 2.1) will depend on the specifics of the implementations. These can vary by bank.

1.3.1.2  Lending The specialized lending solutions have significant depth, relative to the core banking solution, and include collateral management and credit scoring. The following are the “extra functionalities” of the specialized lending solutions: (i) Corporate loan cycle management

(a) Credit appraisal



(b) Collateral management (ii) Across retail and corporate lending



(a) Workflow management



(b) Rules-based processing, including decision-making support (iii) Specific to retail lending



(a) Front office 1) Selling and marketing (b) Retail loan origination



1) Loan application origination via internet; via mobile and lead management



2) Credit scoring



3) Loan product configuration – payment schedule, interest calculation



4) Collateral management

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1.3.1.3  Trade Finance The trade finance product suite is explained under Section 1.2.2.4, while this section highlights the main risks found in trade financing. Trade finance products, like other lending products, have a credit risk exposure and can impact liquidity risk. The funded trade products are sensitive to interest rate movements and currency risks. Trade finance systems provide account-limit management but have very limited functionality to manage liquidity risks, calculate default probability, or set credit VaR limits. The data from the trade finance system is extracted and uploaded into downstream risk management solutions for risk identification and mitigation at a later point in time. There are several risks inherent to the trade financing business.

Country Risk The factors usually associated with this risk type are levels of political and economic stability in the countries that are linked to the contract. This is related to the honoring of payment commitments on time.

Money Laundering Financial Action Task Force (FATF) has emphasized that trade-based laundering is a popular corporate technique for money laundering. Domestic laws and regulations define the scope and depth of the checks required in the system. These include checks on names of individuals and other entities on FATF and other money-laundering hot lists.

Bank Risk The credit worthiness of the banks involved in a trade-financing transaction is a risk factor. The rating of a bank can be influenced by their past conduct for •

delaying or actually reneging on payment;

•

rejecting documents; or

•

a transaction failure because of a country risk (e.g., foreign exchange restrictions and moratoriums).

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Fraud Trade finance is a document-intensive activity, and an error in document management can lead to counterparty fraud, insurance scam, or cargo theft.

1.3.2  Core Banking System A core banking system can meet the requirements of a small bank. The product coverage for the treasury, corporate, retail, and finance (G.L.) divisions, as well as their functionalities, is limited. Core banking systems evolved in the branches and are now centrally hosted solutions. The main features are as follows: •

Front office includes integration with internet banking, mobile banking, e-wallets, digital assistants and social media banking

•

Retail loans and deposits

•

Corporate banking includes trade finance and commercial loans and deposits

•

Treasury module covers front-office deal capture, basic risk management, and back-office settlement, including nostro reconciliation

•

Payments module manages incoming and outgoing transfers; domestic and international payments

•

Limits management

•

General ledger, customer information and reporting

Many “experts” predicted the demise of branch banking after the proliferation of internet banking. Although digital banks lacking a physical branch network are now in business, there has not been a visible trend in branch closures for traditional banks. On the contrary, international banks with branch networks in non-core markets have sold them to large domestic banks looking to expand their customer base and geographical footprint. After Facebook’s breach of trust case, many countries have seen only limited interest in social media banking. However, banks do gather data from social media conversations to obtain market feedback on their services or to validate the market perception of the bank. 48

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1.3.3  Domestic and International Payments Direct Payment using Payment Gateway The gateway refers to the technology that moves money between a merchant account with a bank and the card user’s bank. The buyer makes a purchase using a credit card through the merchant’s card reader or an e-commerce site. The gateway routes the transaction to the correct payment switch, and the switch routes the transaction to the correct issuing bank for approval. Payment gateways allow merchants to accept card payments by connecting payment processors and merchant account providers.

Real-Time Gross Settlement (RTGS) The main components of domestic clearing and settlement processes include settlement, accounting, liquidity management, queue management, and gridlock resolution. From a bank’s perspective, the following issues can surface during checkclearing operations: (i) alterations; (ii) irregularities, and (iii) time-related defects.

S  WIFT Banks that are SWIFT participants remit funds internationally by using specific message types (MT). The SWIFT MT categories include payments, trade services, securities, and trading. SWIFT messages consist of five blocks of data, including three headers, message content, and a trailer. They are identified in a consistent manner. They all start with the literal “MT,” which is followed by a three-digit number that denotes the message type, category, and group. Most established transaction capture systems are SWIFT certified and provide an interface with SWIFT for incoming and outgoing messages.

1.3.4  Systems Owned by Other Functions The data in these systems are part of enterprise data management and contain data for risk and performance management.

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Sales & Marketing In the last decade, banks have successfully leveraged technology to identify prospects, record the interaction, profile the interaction data, and use the information to build advertisements for different customer segments or to improve training programs. Front-­ office functions include cross-selling, lead management, and customer on-boarding. Loyalty programs have a higher probability of success when they are designed using customer-profile and local-market data. Many banks outsource the sales and marketing services of their credit card and consumer loan businesses.

Finance The chief financial officer and his or her team has ownership for the accuracy of the books of account. Thus, the charts of accounts and all issues related to the business’s bookkeeping are owned by the finance division. The general ledger system is owned by the bank if it is a separate system. Banks that use core banking systems (CBS) assign the ownership of the chart of accounts to the finance division, but the CBS is owned by the head of operations and technology.

Human Resources Several analyses of high-profile operational risk incidents have established that the staff is one of the main causes of such incidents. With better data management techniques and advanced analytics, banks are attempting to create a 360° view of their staff so as to improve productivity and minimize risks.

Premises (falls under Operations) Facilities management is now aligned with enterprise risk management and banking business goals. Some evolving aspects of premises management include having green buildings and implementing branch renovations in keeping with the changing lifestyles of their customers. Business continuity planning has a dependency on facilities management.

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Procurement (can be part of the Finance Division) Many large banks have streamlined their operations and use a centralized automated procurement center for all business needs.

Legal The legal departments of several banks use applications that interface with domestic court-data repositories. The work processes of the legal department are now integrated within governance, risk, and compliance (GRC) processes. Some of the department’s core initiatives include the following: •

Embedding legal compliance into banking business processes

•

Training employees on applicable laws and regulations

•

Implementing preventive and detective controls for ensuring legal compliance

•

Integrating with enterprise content and document management

Governance, Risk & Compliance These systems are related to risk model testing and other aspects of risk management.

IT Governance System (falls under Operations) Mid-sized and large banks have IT governance applications that include the management of IT assets, helpdesk management, incident and change management, configuration, and release management. The data in the enterprise IT governance system is part of enterprise data management.

1.3.5  Other Systems 1.3.5.1  Costing Many banks do not have an enterprise cost-allocation system. Smaller banks use spreadsheets or other rudimentary systems to manipulate data from their general ledgers, and the output is erroneous. The lack of accurate data on the cost of doing business has an adverse impact on the pricing of products and services. However, some 51

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international banks have implemented cost-management systems using activity-based costing methodology. However, they face significant challenges in gathering granular, wholesome data.

1.3.5.2  Funds Transfer Pricing (FTP) All three lines of business have sources and applications of funds. However, the treasury is the hub for enterprise liquidity management, and funds transfer pricing (FTP) is a core treasury function. The deposits mobilized by retail and corporate banking are placed with treasury. The funding for the loan books of corporate and retail banking is provided by the treasury. This is illustrated in Figure 1-17.

Figure 1-17.  Elementary schematic of funds transfer pricing The treasury is a centralized funding and liquidity management center. Its responsibilities include liquidity pricing and managing maturity mismatches, among other control functions. The objectives of FTP are to improve the quantitative aspect of efficient product pricing and profitability management. Funds transfer pricing is linked to liquidity and balance sheet management. Figure 1-18 provides an overview of these four factors.

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Figure 1-18.  Centralized funds transfer pricing “Balance sheet management” refers to planning the structure of the balance sheet and mitigating the risks linked to that structure. It ensures an efficient allocation of capital. The most common cause for liquidity risk is a mismatch in maturities between assets and liabilities. A prolonged liquidity problem can lead to a solvency risk.

Funds Transfer Pricing Framework The Asset Liability Management (ALM) policy might allow managers to negotiate favorable rates for key customer relationships. However, competitive pressure could necessitate a risk-based, customer-­specific approach. The following factors have a bearing on loan pricing: •

Credit risk of the loan

•

Cost of funds and the spread required for financing the loan

•

Market rates offered by the competition

•

Maturity and repayment terms of the loan

•

Length of loan amortization period

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The repricing schedule refers to interest-sensitive assets, liabilities, and off–balance sheet positions grouped within time buckets. The repricing gap analysis •

identifies a reference rate;

•

estimates the interest rate sensitivity of assets and liabilities with respect to changes in the reference rate; and

•

calculates an “adjusted gap” that would be indicative of the actual change to the bank’s Net Interest Income (NII), when there is a change in the market reference rate.

What Is Transfer Priced? Funds available with lines of business and the the need for funding by the lines of business, are core elements of funds transfer pricing. The asset and liability products of the corporate and retail banking divisions have different interest rate and maturity characteristics. These are the basis for a transfer pricing mechanism.

The Transfer Pricing Curve One of the most critical aspects of the FTP mechanism is the selection of an appropriate transfer pricing yield curve. A commercial bank could use the funding rate or the investment rate. It should make an assessment of its asset funding and construct a funding yield curve that best reflects its assets. When deciding which rate to use for their curve, banks have the following choices: (i) Libor curve; (ii) treasury yield curve; or (iii) interbank swap curve. Banks operate in a multi-currency environment. Hence, there is merit in considering the application of a single-benchmark yield curve so that it can allocate a yield curve for each currency. The Asset Liability Committee (ALCO) has to make a call on whether to use a single-benchmark yield curve or a multiple-benchmark one. Best practices include policies and procedures that help the bank avoid situations where a line of business “hoards” long-term highly illiquid assets and has few long-term stable liabilities with which to meet funding demands as and when they become due. The following factors influence the FTP framework: (i) prepayment penalty; (ii) term liquidity; (iii) institution credit risk; (iv) option pricing; (v) central bank’s mandatory reverse deposit requirement; and (vi) interest payment.

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Pricing Approaches Table 1-20 provides an overview of the different pricing approaches available to a bank.

Table 1-20.  Pricing Approaches

Figure 1-19 illustrates the loan-rate composition, which includes the base, spread (liability spread), credit risk (credit spread), and margin (funding spread).

Figure 1-19.  Loan-rate composition

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•

Cost-plus and profit-based pricing could entail reducing the price to retain and add to the customer base, with the knowledge that the move could result in a loss or reduced profit margin.

•

Profit-based pricing encourages the increase of prices to preserve overall margins, recognizing that there could be a loss in the customer base.

•

Floating-rate loans are an example of index-based pricing.

•

“Market-based” refers to established theories. In the case of economic theory, it states that price is set where supply meets demand. If lenders can raise price to increase margins, then banks will make more profit and new banks or non-banks will enter the market, altering the supply–demand equation.

•

Risk-based pricing, by definition, allows banks to vary the price based on the credit risk. It is also called differential pricing by customer risk.

•

Adjusted-risk-based pricing takes into account market, credit, liquidity, and operational risks.

There are four FTP methods available to a bank, and the ALCO decides on the methodology. Table 1-21 provides a comparison.

Table 1-21.  Funds Transfer Pricing Methods

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The four FTP methods2 are explained here: •

The single-pool method is mostly used by small banks, as they have undiversified sources of funding. The funds for loans are sourced from deposits, and this method uses a uniform funds transfer rate for both assets and liabilities. It ignores the term and repricing factors associated with products. The main limitations of this method are (i) it ignores the risk of maturity mismatch; and (ii) the risk of repricing.

•

The double-pool method creates (i) an asset pool; and (b) another pool for liabilities. The method ignores the liquidity characteristics of individual products, as the pricing is done at the pool level.

•

Under the multiple-pool approach of FTP, assets and liabilities are classified into different pools using criteria such as maturity, the embedded optionality, and credit. The transfer rates are based on the characteristics of each pool.

•

The matched-maturity approach captures the contribution margin of every contract. The bank assigns a TP rate for each contract based on the specific maturity and the expected cash flows. The transfer rate is assigned based on a specific yield curve, which represents the bank’s ability to source funds of various maturities from the interbank market. As transfer rates are at the contract level, pricing is more accurate than in the multiple-pool approach.

Data Dimensions of FTP The following points are mentioned so as to aid in understanding the data management requirements of an FTP system: •

Spread for the computation of the transfer prices over the benchmark rate is assumed to include elements such as cost of fund, spread over benchmark rate, liquidity spread, and overhead cost spread.

 Funds Transfer Pricing in Banks – Reserve Bank of India, https://www.cafral.org.in/ sfControl/content/LearningTakeaWays/827201455252PMPaper_Funds_Transfer_Pricing_in_ Banks.pdf

2

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The real-life scenario in many banks is that there is constant friction between the treasury and other lines of business (LOBs) regarding the transfer rates. The quality of data is one of the core reasons for the lack of agreement. The data constraints include the following: •

The data used by the treasury is disputed by the LOBs.

•

Granular historic data is unavailable for pricing.

•

There is weakness in reconciling financial data with risk data.

Funds Transfer Pricing System Implementation The implementation has a dependency on the number of systems from which data has to be extracted. In some large banks, the FTP system is seen as part of the ALM system (figure on the right-hand-side in Figure 1-20), and the treasury takes ownership of the systems. In other banks, there are two separate but “integrated” systems.

Figure 1-20.  Funds transfer pricing implementation scenarios

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Adjustments in Transfer Pricing The funding curve might require adjustments as the transfer pricing curve might not reflect the unique attributes of some of the financial instruments of a bank, or the curve might be inconsistent with the bank’s balance sheet structure. Credit risk adjustment might be required if a bank is not “deposit rich.” The ALCO should provide clarity on the following requirements, and the FTP system should be configured accordingly: •

The treasury might need compensation in the form of a funding commission adjustment.

•

The central bank’s statutory reserve requirement makes a deposit requirement adjustment necessary.

•

Option pricing adjustment is necessary as there is a cost attached to the customer’s right to change the contractual terms of a transaction.

FTP systems have the ability to •

analyze repricing behavior;

•

estimate deposit retention in stressed and normal scenarios;

•

assess the interest rate sensitivity of a balance sheet; and

•

manage potential adverse optionality movements.

Table 1-22 provides an overview of the interest rate repricing reference rate of some products. The behavior of non-maturing deposits has a significant impact on liquidity management. ALM, balance sheet, and liquidity management are explained in Chapter 2, Section 2.3.

Table 1-22.  Data for Funds Transfer Pricing

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Efficient Product Pricing All business entities strive to differentiate their products to attract customers. FTP is core to pricing strategies and supports the proactive management of interest rate risks. Good-quality data is needed for decision making that prevents or minimizes structural liquidity issues.

Profitability Management Table 1-23 provides a template for determining product profitability. A bank manages the profitability of its lines of business and products by implementing measures to improve the net interest margin. A key function of the treasury, with support from ALCO, is to manage the cost of funding. Banks try to manage the uncertainties of interest-based income by diversifying their products and services to include fee-based income.

Table 1-23.  Loan Pricing and Profitability Template

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Banks have used a product-centric approach for the implementation of the preceding solutions. They extract, transform, and load their fragmented data from disparate systems for analytics, risk management, and reporting. Chapter 3 further explains the siloed nature of the operating environment and its impact on the bank. Chapter 10, Section 10.3 explains liquidity transfer pricing (LTP), as an improvement to funds transfer pricing.

1.4  Evolution of Basel Risk Management Recommendations 1.4.1  1988 Basel-I Basel I defined capital adequacy and created more awareness on prudent capital management. In 1988, the Basel I Capital Accord set forth the minimum capital requirements (MCR) for banks. The MCR is an amount determined to be commensurate with the risk exposure of the bank and is expected to act as a buffer against losses. The accord divided the balance sheet of a bank into five categories, and different risk weights were assigned to the assets based on its credit risk. It set a minimum capital ratio of 8%, calculated using regulatory total capital and the risk-weighted capital. Banks were encouraged to use credit default swaps to hedge their credit risk and lower the credit risk exposure.

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Table 1-24.  Basel I – Positive Aspects, Weaknesses

1996 Market Risk Amendment (1988 Accord amendment) In 1996, Basel Committee on Banking Supervision (BCBS) published an amendment that provided a capital cushion to cover price risks arising from trading activities. Salient features of the amendment were as follows:

62

•

Banks were allowed to use internal models (i.e., proprietary) for measuring market risks and VaR computations.

•

It introduced a capital charge for banks that use a proprietary model. The amount is the higher of the previous day’s VaR and three times the average of the daily VaR of the preceding sixty business days. It recommended back testing for calculating the “plus factor” that is added to the multiplication factor of three.

•

Introduced the standardized measurement approach in which the general market risk and specific security risk are calculated separately and totaled.

•

Banks were required to segregate their trading and banking books. It introduced the mark-to-market process for all portfolios in the trading book.

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First-Generation Credit Risk Management Models The first-generation credit risk management models are explained here and not in the next chapter so as to keep the focus on the evolution of risk management requirements, the models, and the data requirements. The four models provided the basis for the evolution of credit risk management, and the concepts are still relevant for credit risk modeling. After Basel I, banks implemented one of the following credit risk management models: CreditMetrics, the KMV, CreditRisk+, or CreditPortfolioView. Risk management veterans refer to the models as first-generation credit risk management models. JP Morgan’s CreditMetrics3 is based on the probability of moving from one credit quality to another, including default, within a given time horizon. CreditMetrics is now owned by MSCI. Figure 1-21 provides a data perspective of CreditMetrics with references to the subsequent chapters that explain credit risk management (not just CreditMetrics).

Figure 1-21.  CreditMetrics framework3 - data perspective

 CreditMetrics™, Technical Document.

3

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In the KMV model, the default process is endogenous and relates to the capital structure of the firm. It is also referred to as the option pricing or structural approach, as it is based on the asset-value model originally proposed by Merton. Default occurs when the value of the assets falls below some critical level. Moody’s acquired the KMV model. CreditRisk+, a Credit Suisse contribution, uses an actuarial approach and focuses on default. Figure 1-22 provides an overview. Default for individual bonds or loans is assumed to follow an exogenous Poisson process.

Figure 1-22.  Credit Risk+ framework McKinsey’s CreditPortfolioView uses a discrete time multi-period model in which default probabilities are conditional on macro-variables like unemployment, the level of interest rates, and the growth rate in the economy. Tables 1-25a, 1-25b, and 1-25c provide a comparison of the four methods.

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Table 1-25a.  Comparison of Credit Risk Management Methods

Most risk management systems offer two approaches—(i) mark-to-market and (ii) default mode—to calculate the portfolio loss distribution and the desired risk measures. The mark-to-market approach is largely based on the CreditMetrics methodology and uses simulation. The default-mode approach is based on the CreditRisk+ methodology. In this approach, only default and non-default are considered as the possible states of credit quality.

Table 1-25b.  Comparison of Credit Risk Management Methods

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From an enterprise data architecture and data management perspective, these tables reveal certain characteristics of risk data that are different from accounting data. The differences are in the data type, character, and usage. It influences the choice of architecture and technology for enterprise risk management. This is explained in Chapters 7 and 8. Table 1-25c.  Comparison of Credit Risk Management Methods

Credit risk management solution implementation is explained in Chapter 2, Sections 2.1.2, 2.1.3, and 2.2. Data is a critical success factor in implementing risk models. This is explained in Chapters 7 and 8. Bank for International Settlements (BIS) released in September 2000 the principles that should be used in evaluating a bank’s credit risk management. Global Derivatives Study, 20 Recommendations4 The Global Derivatives Study, Group of 30 Report offered twenty recommendations for derivative risk management. The key ones under the five categories are mentioned here: •

Governance

Recommendation 1 – Role of senior management

4

•

Market Risk Management

Recommendation 2 – Mark to market Recommendation 3 – Market valuation methods

4 4

 Derivatives: Practices and Principle: https://group30.org/images/uploads/publications/ G30_Derivatives-­Appendix_1.pdf

4

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Recommendation 4 – Identifying revenue sources. It advised traders to measure the components of revenue regularly and identify the sources of risk. This included origination revenue, credit spread revenue, and other trading revenue. 4 Recommendation 5 – Measuring market risk; VaR and greeks were important components 4 Recommendation 6 – Stress simulations 4 Recommendation 7 – Investing and funding forecasts 4 Recommendation 8 – Independent market risk management; referred to organization structure; policies, emphasis on stress tests and volatility measurement, review and approval processes for pricing models 4

•

Credit Risk Measurement and Management

Recommendation 10 – Measuring credit exposure Recommendation 11 – Aggregating credit exposures, taking into consideration enforceable netting arrangements 4 Recommendation 12 – Independent credit risk management referred to approving credit exposure measurement standards; setting credit limits and monitoring their use; reviewing credits and concentrations of credit risk; and monitoring risk reduction arrangements. 4 Recommendation 13 – Master agreements 4 Recommendation 14 – “Credit enhancement” refers to (a) collateral and margin arrangements; (b) third-party credit enhancement such as guarantees or LCs; and (c) structural credit enhancement using special-purpose vehicles (SPV) for managing derivatives business. 4 4

•

Enforceability

Recommendation 17 – Systems automation should be efficient and adequate for front-office deal capture, risk management, and backoffice functions. Risk management should be comprehensive, and the scope should include the risks arising from the use of derivatives. 4

•

Accounting and Disclosure

Recommendation 19 – Accounting practices Recommendation 20 – Disclosures

4 4

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1.4.2  2004 Basel II The three pillars introduced in Basel II5 are (i) minimum capital requirement; (ii) supervisory review; and (iii) market discipline (see Table 1-26). The following market risk changes have an impact on the trading book: •

Capital charge is computed as the sum of four components: (i) net short or long position; (ii) small proportion of matched positions for each time bucket; (iii) larger proportion of matched positions across different time buckets; and (iv) net charge for positions in options. Basel mandated banks to have documented trading strategies; active position monitoring for all trading desks, including position limit setting and monitoring; and mark to market of all positions daily.

•

Capital charges for specific interest rate–linked risks

•

Specific charge for positions hedged using credit derivatives

•

New counterparty credit risk charge for OTC derivatives, repo-style, and other transactions booked in trading book

•

Adjusted add-on factors for single-name credit derivatives

Table 1-26.  Basel II Pillars 1, 2, and 3

 Basel II: The New Basel Capital Accord, 29 April 2003, https://www.bis.org/bcbs/bcbscp3.htm

5

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Market Risk – Standardized Measurement Method6 The standardized measurement method for interest rate risk measurement allowed banks to use either the maturity method or the duration method. The following are the important aspects of the methods: Maturity method:6 •

Opposite positions of same amount in the same issue can be netted.

•

Positions are weighted using price sensitivity factor.

•

There is a partial offset for weighted longs and shorts in each time bucket.

•

There are two rounds of horizontal partial offsetting between time buckets.

Duration method:6 •

Price sensitivity is calculated using prescribed interest rate change.

•

Sensitivity measures use duration-based ladder within time buckets.

•

There is vertical disallowance on long and short positions.

•

You can carry forward net positions for horizontal offsetting.

The standardized measurement method6 for FX risk is related to the (i) exposure in single currency position and (ii) risks inherent in the mix of long and short positions in different currencies. These risk types are as follows: •

Equity Risk – Specific risk charge was defined at the gross equity position and marked to market (MtM). The general market risk charge was defined as the difference between the sum of longs and sum of shorts, and marked to market.

•

Commodity Risk6 – Defined as risks associated with a physical product that is traded in the secondary market. Risks include basis risk, interest rate risk, and forward gap risk. Additionally, commodities trading involves a directional risk. It is the financial risk due to a change in the spot price.

 Amendment to the Capital Accord to incorporate market risks, https://www.bis.org/publ/ bcbs119.pdf

6

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Table 1-27 provides a summary of the core aspects of the Basel II recommendations. In the left column is the risk type or function, and in the right column there is the summarized point(s) that was part of the Basel II requirement scope.

Table 1-27.  Basel II Functional Coverage Summary

Basel II provided three approaches for credit risk measurement5: (i) the standardized approach; (ii) the Foundation Internal Ratings–based approach (F-IRB); and (iii) the Advanced Internal Ratings–based approach (A-IRB). Table 1-28 provides a summary of the three approaches.

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Table 1-28.  Basel II Credit Risk Management Approaches and External Rating Criteria

The standardized approach5 measures credit risk using fixed risk weights that are based on external credit assessments (ratings). Foundation IRB4 measures credit risk with sophisticated formulas that use internally determined inputs of probability of default (PD), inputs fixed by regulators of loss given default (LGD), exposure at default (EAD), and maturity (M). Advanced IRB5 measures credit risk using sophisticated formulas and internally determined inputs of PD, LGD, EAD, and M. The Foundation approach differs from the advanced approach in its usage of internal risk estimates to compute capital requirements (Table 1-29). The Foundation approach requires the bank to use the risk inputs provided by respective supervisors to obtain the LGD, EAD, and M factors. The Advanced IRB approach allows a bank to use internal risk inputs for all four factors and is more risk sensitive to changes to credit exposures than the standardized or Foundation IR approaches.

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Table 1-29.  Economic Capital for Credit Risk – Comparison of Methods

•

Probability of default (PD)5 was defined as the probability that a borrower would default over a one-year period. In the Advanced IRB approach, banks estimate the PD for each borrower grade and for each asset class. PD estimates the chances that a customer would make payments on time or would remain solvent during the period of borrowing.

•

PD models are broadly divided into two categories: (i) point-in-time probability of default (PIT-PD) and (ii) through-the-cycle probability of default (TTC-PD)7. Probability of default depends on the risk characteristics of the obligor and the macroeconomic conditions. The PIT-PD7 model uses current macroeconomic conditions and risk characteristics of customers. The PIT-PD goes up as macroeconomic conditions deteriorate and goes down as macroeconomic conditions improve. The TTC-PD model models the obligors’ risk characteristics while keeping the macroeconomic conditions static.

 Designing and Implementing a Basel II–Compliant PIT-TTC Ratings Framework, Scott D. Aguais, Lawrence R. Forest Jr, Martin King, Marie Claire Lennon, Brola Lordkipanidze, Barclays Capital, 27 January 2008.

7

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•

Loss Given Default5 is the amount a bank expects to lose should a default occur. This allows a bank using the Advanced IRB approach to recognize credit mitigants such as collateral, guarantees, and hedges. The banks should be able to convince their regulator of the tangible benefits of risk mitigation.

•

Exposure at Default5 is applicable to on– and off–balance sheet exposures and was defined at the facility level. For off–balance sheet exposures, a bank applies credit conversion factors (CCFs) to the unused exposure amount in order to generate an EAD.

•

Maturity5 is a driver of credit risk. There is empirical evidence that a shorter maturity brings a lower capital requirement than a longer maturity.

Operational Risk5 For operational risk management, the basic indicator, standardized, and advanced measurement approaches (BIA, TSA, and AMA) were introduced. The Basel II Accord defines operational risk as “the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events.” The root cause could be any one or more of the following: (a) the bank’s risk culture; (b) systems; (c) staff conduct or performance; (d) policies and procedures; and (e) external sources. a) Risk Culture The risk culture manifests itself in the business approach. Examples include having a “too big to fail” business approach, unfair practices, indifference toward regulatory compliance, complex product offerings, and aggressive selling of products and services. b) Systems (Automation) The financial industry is one of the largest users of technology. The big banks are early adopters of emerging technology, and some of them collaborate with banking solution vendors to be in the forefront of leveraging technology. Enterprise IT governance 73

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minimizes risks associated with automation. The alignment of technical processes with business processes to attain business goals is a critical success factor. c) People Staff play an important role in building trust and in improving the customer experience. They could also be a source of operational risk and create a financial loss for a bank. The cause of the risk event might be traced to a fraudulent intent or linked to inadequate staff skills. d) Policies and Procedures e) External Sources These would include Acts of God and incidents triggered by parties not having any relationship with the bank (e.g., hackers). Examples include earthquake, flood, fire, hacking, and robbery. Basel II recommended three approaches for determining the Minimum Capital Requirements (MCR) for managing operational risks: (i). Basic indicator approach calculates the capital charge based on a single indicator for the whole bank. (ii). Standardized approach uses a combination of business lines and financial indicators. (iii). Advanced measurement allows the bank to use internal loss data for modeling the operational risk and computing the capital charge.

Basic Indicator Approach5 In this approach, the bank was allowed to set aside a fixed percentage of the average annual gross income over the previous three years. The gross income was computed as the Net Interest Income + Non-interest Income, (i) prior to any provisions; (ii) gross of operating expenses; (iii) excluding extraordinary or irregular items; excluding income

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from insurance; and (iv) excluding realized profit or loss from the sale of securities in the banking book. The fixed percentage is 15%. The BIA used revenue as a proxy for scale, and the risk was calculated at the institutional level.

The Standardized Approach (TSA)5 The TSA also used revenue as a proxy for scale, and the risk was calculated at the business-­function level. The gross business income of the eight business lines is multiplied by the fixed percentage recommended for each of the business functions, as shown here: i. Corporate Finance – 18% ii. Trading & Sales - 18% iii. Payment & Settlements – 18% iv. Commercial Banking – 15% v. Agency – 15% vi. Retail Banking – 12% vii. Asset Management – 12% viii. Retail Brokerage – 12% The weaknesses of the preceding two approaches include the following: •

Income is not a good yardstick to measure operational risk;

•

The approaches •

did not attempt to differentiate between efficiency levels in operations; for example, they ignored a process-based efficient model;

•

ignored risk mitigation factors such as insurance; and

•

did not encourage a bank to improve the operating model, reduce costs, or minimize risk.

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Advanced Measurement Approach (AMA)5 AMA required a bank to demonstrate that its operational risk measurement was comparable to the internal ratings-based approach for credit risk; i.e., one-year holding period and an around 99.9th percentile confidence interval. The qualitative aspect of the approach was about the review of the operational risk measurement system. The quantitative aspect used (i) internal data; (ii) external data; (iii) scenario analysis; and (iv) business environment and internal control factors. The key advantages of the approach included (i) the categorization of losses as expected and unexpected; (ii) it allowed the use of external data and scenario analysis; (iii) recognized operational efficiency; and (iv) made it easier for banks to move toward enterprise-wide risk management.

In 2017, Basel dropped the three approaches for operational risk management recommended in Basel II and introduced a new approach called the standardized measurement approach (SMA). This is explained in Chapter 3, Section 3.2.3.4, which discusses gap analysis. Looking beyond compliance, several operational risk management experts recommended the AMA approach as it is a feasible and effective approach for improving efficiency, reducing costs, and minimizing operational risks. Figure 1-23 illustrates the core aspects of Basel II recommendations for market, credit and operational risk management.

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Figure 1-23.  Basel II capital adequacy The second pillar guides the bank on risk management, supervisory review, and transparency.

Principles of Supervisory Review and Evaluation The key principles for supervisory review and the evaluation process are as follows: •

Banks should have a process for assessing their capital adequacy in relation to their risk profile and a strategy for maintaining the required level of capital.

•

The supervisor •

should review, evaluate, and monitor the capital adequacy of banks to ensure compliance with regulatory capital ratios;

•

will ensure that banks operate above the minimum regulatory capital ratios. Banks could be instructed to hold capital in excess of the minimum ratio; and

•

can intervene at an early stage to prevent the capital level from falling below a minimum level. Rapid remedial supervisory actions might be required if capital is not maintained or restored. 77

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Basel 2.58 In the period between Basel II and Basel III, the Basel committee recommended the following: •

introduction of a comprehensive risk measure (CRM) to correlate trading positions so as to assess underlying exposures for default and migration risks

•

using stressed VaR. The measure uses a one-year historical dataset that encompasses a continuous 12-month period of significant financial stress relevant to the bank’s portfolio.

•

introduction of the incremental risk charge (IRC) for managing the impact of credit margin and default risks

•

a charge for securitization and re-securitization of positions

Incremental Risk Charge – IRC9 One of the revisions to the Market Risk Amendment was an incremental risk charge (IRC) requirement to cover credit risk in the trading book and risk arising from illiquid products that were not reflected in the VaR.

1.4.3  2010 Basel III The 2008 financial meltdown had a profound impact on Basel III requirements. It was widely believed that capital adequacy enforcement was inadequate. Three important findings from the analysis of the meltdown were (i) complex derivatives allowed excess leverage; (ii) banks relied on external credit risk assessment (rating agencies); and (iii) some internal risk models were found to be ineffective.

 Basel 2.5, https://www.bis.org/publ/bcbsca.htm  Guidelines for computing capital for incremental risk in the trading book, https://www.bis. org/publ/bcbs159.pdf

8 9

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The three primary objectives of Basel III were to (i). provide the banks with the capability to withstand shocks; the capability should have some preventive and intelligent features that would significantly minimize the contagion effect on the domestic economy and a ripple effect globally; (ii). encourage banks to transform their operating model to enable enterprise risk management; and (iii). improve the governance in banks, with transparency, accountability, and disclosure practices as key drivers. The Basel Committee felt that Tier 1 capital reporting needed significant improvement. Tables 1-30a and 1-30b provide an overview of the Basel III pillars.

Table 1-30a.  Basel III Pillar 1 Requirement Overview

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Table 1-30b.  Basel III Pillar 2 and Pillar 3 Requirement Overview

The following are the core Basel III reforms for improving the quality and consistency of capital: 10

Tier 1 capital should contain mostly ordinary shares termed Common Equity Tier 1 (CET1). Additional Tier 1 capital must be capable of supporting a bank on an ongoing basis. Common equity requirement was raised to 4.5% from 2%;

•

10

•

10

•

10

•

10

The Capital conservation buffer is an additional reserve buffer of 2.5% to withstand stress. This makes the total Tier 1 capital reserves required as 7%. If credit is expanding faster than GDP, the banking regulator could increase the capital requirements with the help of the countercyclical buffer or other fully loss-absorbing capital. Tier 3 capital was removed.

 Basel III: A global regulatory framework for more resilient banks and banking systems, revised version, June 2011, https://www.bis.org/publ/bcbs189.htm

10

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Figure 1-24 illustrates the elements of a Bank’s capital and its sub-components as per the Basel III recommendation.

Figure 1-24.  Basel III – Elements of a bank’s capital10

R  estricted the Leverage10 •

A non-risk-based leverage ratio of 3% was introduced as a supplementary measure to the risk-based requirement.10

•

Capital requirements should be determined using “stressed” inputs when calculating counterparty credit risk.10

•

Banks must implement a credit value adjustment (CVA) to cover the risk of mark-to-market losses on the expected counterparty risk to OTC derivatives. This is in addition to the default risk capital charge.10

Table 1-31 provide a comparison of the focus areas of Basel II and Basel III.

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Table 1-31.  Basel II and Basel III Comparison

An Overview of Liquidity Management under Basel III The new multi-currency liquidity paradigm is influenced by the lessons from the 2008 crisis and covers long-term structural funding and short-term liquidity. The two liquidity standards that Basel III provides are the Net Stable Funding Ratio (NSFR) and its complementary Liquidity Coverage Ratio (LCR).

Net Stable Funding Ratio (NSFR)11 The Net Stable Funding Ratio (NSFR) promotes the funding of business activities with relatively more stable funding sources. Banks are required to fund long-term assets with reliable sources of funds that have an effective (residual) maturity greater than one year. Banks are required to classify their equity, liabilities, and assets with an effective maturity of greater than one year, at an individual account or position level, into either available stable funding or required stable funding, based on their liquidity behavior under stressed conditions.

 Basel III: the net stable funding ratio, https://www.bis.org/bcbs/publ/d295.pdf

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The core NSFR requirements are as follows: •

Document the cash-flow records for the lifecycle of instruments.

•

Have a consistent and logical view of all assets and liabilities in the balance sheet.

•

Configure transparent rules for classification and computation of NSFR.

•

Monitor the encumbrance profile of all the assets and collaterals for securities transactions.

•

Ensure that short-term liquidity ratios are consistent with longerterm stable funding and lending.

Liquidity Coverage Ratio (LCR) Overview12 The liquidity coverage ratio (LCR) requires a bank to maintain an adequate level of unencumbered, high-quality liquid assets (HQLA) that could be converted into cash to meet its liquidity needs for a 30-day time horizon under an acute liquidity stress scenario specified by supervisors. The standard requires that the ratio be no lower than 100%. Hence, banks must maintain a buffer in the form of HQLA sufficient to withstand the cash outflow during this period. This arrangement provides a bank with sufficient time to resolve the issue at hand. The LCR must be measured and reported to the regulators as a simple average of daily observations over the previous quarter. Banks monitor the LCR for all significant currencies and can transfer a liquidity surplus in one currency to mitigate a liquidity deficit in another currency during a stress period.

The efficient management of LCR and NSFR is critical for enterprise liquidity management. This has a profound impact on enterprise architecture and data management. This aspect of the target architecture is covered in Chapters 5, 7, 9, and 10. Table 1-32 provides a view of Basel III changes by risk type and by the three pillars introduced in Basel II.  Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools, https://www.bis. org/publ/bcbs238.pdf

12

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Table 1-32.  Basel III Builds on Basel II Pillars

Table 1-33 provides a summary of the evolution of Basels I, II, and III.

Table 1-33.  Basels I, II, and III Comparison13

The next chapter explains the implementation of risk management solutions in commercial banks. The data for the risk management systems is obtained from the systems explained in this chapter, Section 1.3.

 History Basel I,II,III, https://www.bis.org/bcbs/history.htm

13

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Siloed Risk Management Systems The as-is environment comprises the source systems explained in Section 1.3 of Chapter 1 and the risk management solutions explained in this chapter. The narrative in this chapter does not follow Basel III requirements, nor does it discuss quantitative risk modeling. There are numerous books on the quantitative modeling of each of the risk types. The objective of this chapter is to describe the different risk management solutions available to a commercial bank, the scope of the implementations, the data requirements for risk management, and the siloed nature of the solutions. In almost all banks, the risk data is fragmented and the environment is “band-aided.” The following is the layout of this chapter: •

Section 2.1 explains market and credit risk management solution implementation for the treasury line of business.

•

Section 2.2 explains credit risk management for the lending business. The term “expected loss approach” is sometimes used to describe the use of probability of default, loss given default, and exposure at default to manage credit risk.

•

Section 2.3 covers asset liability management (ALM).

•

Section 2.4 explains anti–money laundering and countering the financing of terrorism (AML-CFT).

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© Kannan Subramanian R and Dr. Sudheesh Kumar Kattumannil 2022 K. Subramanian R and S. K. Kattumannil, Event- and Data-Centric Enterprise Risk-Adjusted Return Management, https://doi.org/10.1007/978-1-4842-7440-8_2

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Siloed Risk Management Systems

Section 2.5 covers operational risk management system implementation. Some banks have a separate system for fraud management. The case studies in Section 2.5.2 explain fraud and money laundering in the context of operational risk management. The 19 case studies, split into five categories, illustrate the multidimensional aspect of operational risk.

Figure 2-1 illustrates the siloed nature of the various risk management solution implementations. It is a depiction of the narration from Sections 2.1 to 2.5. The bottom layer illustrates the transaction/contract capture systems. They provide data to the risk management systems that form the top layer of the figure.

Figure 2-1.  Siloed risk management systems – a schematic There is a lot of functional overlap, and the quality of data used by the risk management systems is unsatisfactory. Banks are at various stages of BASEL III implementation. Their policies, methodologies, procedures, and processes are influenced by the BASEL evolution explained in Chapter 1. The siloed nature of the environment can also mean that the bank has not fully evolved consistently with the BASEL requirements.

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Common Functions in Risk Management Systems The market and credit risk management systems, and the anti–money laundering and fraud “management” systems, all process granular data. The ALM system works on aggregated data. Operational risk, by its definition, encompasses all the systems, procedures, and processes. The core components of a risk management solution are as follows: a) Instruments/Products contain the data of the individual or aggregated exposure that make up the portfolios. Treasury risk management functions include attaching pricing models to the instruments and using fair value, market value, and theoretical value. b) FX contains the data for currency conversion rates and the FX curves. c) A portfolio is a grouping of financial instrument/product exposures; e.g., bond portfolio or loan portfolio. The grouping is done to facilitate the analysis of the aggregate behavior of the exposures. Positions, cash, and settlement accounts are attached to a portfolio. d) Curve definitions specify the attributes of instruments or products and values. e) The preceding four components are the “granular components” of the treasury’s market, credit, and liquidity management. The credit risk management of the loan book is done separately. f ) Models are built by using pricing and risk functions. The ALM and operational risk management models are different from market, credit, and liquidity measurement models. g) Scenarios use hypothetical assumptions about the future state of relevant factors such as economy, market, or business sector. h) Stress tests can be scenario-based simulations that allow the bank to gain insight into their risk exposure over a user-defined time.

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The common elements are illustrated in Figure 2-2.

Figure 2-2.  Risk management systems – core components

Risk management systems are “number-crunching” software solutions. After the initial configuration of the system, relatively speaking, there is limited user interaction with risk measurement processes. The data is uploaded/imported either from source systems or from external vendors. The processing functions are driven by risk management procedures, data relationships, volume, velocity, and variety. The siloed as-is environment is characterized by data plumbing, whereas this book’s recommended target environment establishes data lineage using relevant technologies.

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Definitions  https://www.bis.org/cpmi/publ/d00b.htm?selection=1 5&scope=CPMI&c=a&base=term Minimum capital requirements for market risk January 2019 (Rev. Feb-2019)— Standards, Terminology: RBS25, MAR10, MAR11, MAR12, MAR20, MAR21, MAR22, MAR23, MAR30, MAR31, MAR32, MAR33, MAR40, MAR90, MAR99.

2.1  Treasury’s Market Risk and Credit Risk Management The chief risk officer has a responsibility for both risks, but the ownership is with treasury. The credit risk in the loan book is managed separately and is discussed in Section 2.2.

2.1.1  Treasury Risk Management System Modules Modules in the System (Market & Credit Risk) Figure 2-3 provides an overview of the data flows from the source systems (below the dotted line), including external data, to the risk management system (above the dotted line). In the siloed environment, credit risk in the loan book (Section 2.2) is measured and monitored separately. In the treasury risk management system, there are some differences between the market and credit risk management processes. Hence, Figure 2-3 shows the two separate streams from Section 2.1.

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Figure 2-3.  Treasury’s market and credit risk management systems Treasury’s implementation of market risk has the following components: a) External data required for risk management explained in Section 2.1.1.1 b) Financial engineering – modeling specification/configuration, internal data requirement

1.

Product - Section 2.1.1.2a



2.

Curve – Section 2.1.1.2b



3.

Portfolio – Section 2.1.1.2c

The templates used in the narrative are illustrative of a financial engineer’s data specifications template.

2.1.1.1  Data Required The main types of market data are as follows:

90

•

Price data – for exchange-traded instruments

•

Interest rate data – The two common types are (a) yield to maturity (YTM) and (b) zero-coupon curves. YTM curves are used in the valuation of bonds and provide an automatic calibration to market

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price. Zero-coupon curve pricing provides a valuation approach that is arbitrage-free and supports value additivity. •

FX data – The generation of curves can be categorized as (a) spot FX rates plus direct market forward rates or (b) spot FX rates with the use of an interest rate parity relationship. In the latter case, the valuation is arbitrage-free.

•

Volatility surface data – Price volatility surface data is required for all option valuation models. Risk analysts and managers prefer volatility surface data as it provides the ability to calibrate theoretical prices to market value and maintain an appropriate skew and smile for changing market conditions.

Table 2-1 provides an example of sourcing external data for measuring the risks in treasury books.

Table 2-1.  External Market Data Sourcing Market Data

Product

Source

Bond Spot

Fixed rate bond

Dow Jones

Interest Rate

T Bill, Bond Option, Equity Option, FX

LIBOR

FRN

Volatility Surface

Bond Option, Equity Option

Internal

Index Spot

FTSE / S&P /

Reuters

Equity Spot

Common

Reuters

Equity Beta

Internal

Equity Option Spot

Reuters

FX Spot

Reuters

Frequency / Timeliness Bank determined

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2.1.1.2  Financial Engineering – Modeling Specification/ Configuration 2.1.1.2a  Product-Model Specification Instrument Modeling Product models include bond model, derivative model, physical asset model, swap model, and synthetic model. Modeling of each financial product or class of products consists of the following steps: •

Conceptual risk measurement

•

Investigation of product characteristics

•

Investigation of market characteristics relevant to the product

•

Mapping of product attributes to system attributes

•

Determining the source for the data; i.e., which internal system or which external data provider

•

Validating the configured model and documentation

For example, a specification could have the following details:

The bond pays regular fixed rate, has no peculiar cash-flow character; it is exchange traded but not highly liquid; use calendar-adjusted cash flows, there is withholding tax and accrued interest accounting; calibration of spot price and daily theoretical valuation function are required; data requirements include price data, interest rate curve, FX, and volatility surface; use the Monte Carlo simulation framework. Table 2-2 is an example of product data mapping from the source system (the treasury management system) to the market and credit risk management system owned by the treasury.

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Table 2-2.  Bond Data Mapping Data Item – Source System Risk System – Data Requirement Product name Source system identifier Product type

Fixed-rate bond 5% 2019/01/04 Fixed-rate bond 5% 2019/01/04 Fixed-rate bond

Currency Notional Coupon rate Accrual day count basis Coupon generation Fixed coupon date Maturity Date Issue Date Business day rule Trading day rule

USD 100 USD 5% Semi Actual/365

Spread over yield Withholding tax: Accrual date Base price Tax rate Discount curve Theoretical model Market model

Implied market spread

Risk Management System

Market & credit risk management system Mapped target data item

Backward If source system adjusts maturity date 2019/01/04 2007/01/04 Regular Following 0-day Regular following 3-day

IRUSD PV bond Bonds market

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Tables 2-3a and 2-3b illustrate the data requirements for modeling bond portfolios.

Table 2-3a.  Portfolio of Bonds Model Definition for a Portfolio Containing Three Fixed-Rate USD Bonds Attribute

Bond 1 Values

Bond 2 Values

Bond 3 Values

Name Currency Coupon rate Notional Maturity date Discount curve THEO/Value THEO/Price THEO/Accrued interest THEO/Yield THEO/Adjusted duration

FRB5.5% 2024/06/07 USD 5.5% Semi Actual/Actual 100.00 USD 2024/06/07 IRUSD 91.40 USD 89.96 USD 1.5124 USD 6.8015% Semi Actual/365 n.nnnn

FRB6.5% 2018/12/12 USD 6.5% Semi Actual/Actual 100.00 USD 2018/12/12 IRUSD 101.43 USD 100.33 USD 3.25 USD 6.3% Semi Actual/365 n.nnnn

FRB7.5% 2020/04/03 USD 5.5% Semi A/A 100.00 USD 2020/04/03 IRUSD 104.34 USD 102.14 USD 2.45% Semi Actual/365 6.55 % Semi Actual/365 n.nnnn

Table 2-3b.  Bond Model Name

Value

Price

PV01

Market price

Clean to dirty

spot

PV01 coupon bond

PV bond yield

PV bond semi-annual interest

PV bond less AI

PV01 coupon bond

Tables 2-4a and 2-4b illustrate the data requirements for modeling currency exposures.

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Table 2-4a.  FX Model Definition Product Market Modeling

Model

Underlying

Volume Market Data

Source FX Spot Options

Interest rate parity Example BlackScholes

Spot, interest rates

Table 2-4b.  FX Conversion From Currency

To Currency

Exchange Curve

USD

GBP DEM FRF DEM FRF

GBP Forward DEM Forward FRF Forward GBP-DEM Forward GBP-FRF Forward

GBP

Table 2-5 illustrates the data requirements for modeling interest rate products, and Table 2-6 illustrates equity and commodity.

Table 2-5.  Interest Rate Product Modeling Data Product

Market

Modeling

Model Source

Underlying

Volume

Market Data

Treasury Bond Treasury Bill FRN

US Treasury US Treasury Corporate

PV CF PV CF PV CF

Bank Or Vendor

No No No

nnn

Bond Spot Interest rate LIBOR

Table 2-6.  Equity, Commodity Modeling Data Product Market Modeling Options

Example BlackScholes

Model Underlying Source

Volume Market data nnn

Spot, beta, interest rate, option spot, volatility 95

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The risk datasets comprise product, curve, portfolio, scenario, and counterparty data items. Product data specifications provide an overview of treasury instrument attributes; i.e., currency, cost of carry, coupon rate, coupon data, effective date, growth rate, reset date, market index, maturity date, notional, payment frequency, rate, payment zero curve, receive frequency, rate, receive zero curve, spot price, strike price, underlying discount curve, and volatility. Here are some examples of basic attributes of treasury financial instruments: •

Zero-Coupon Bond •

•

European Call or Put •

•

Name, ID, coupon rate, maturity date, notional, discount curve, spot price

Name, currency, strike price, ID, underlying, maturity date

Bond Future •

Name, ID, currency, issue date, maturity date, discount curve

Table 2-7 provides an overview of Treasury Market and Credit Risk data.

Table 2-7.  Treasury Market and Credit Risk – Data Types and Risk Factors

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Product modeling consists of the following important steps: •

Risk measurement view

•

Product characteristics

•

Modeling requirements and their configuration in the risk management system. This drives the cash-flow generation and valuation results.

2.1.1.2b  Curve Specifications Overview of Different Types of Curves A curve is a tool for valuing financial instruments and involves interpolation and calibration. “Data point” refers to the coordinate that is used to plot a curve. For interpolation, the core elements are the linear data points and the interpolation entity. Calibration requires the instrument valuation formula and a calibration algorithm. •

Par Curve1 The curve gives the YTM for coupon-paying bonds at each maturity. The nominal spread is the difference in the YTMs of two bonds with the same maturity. It is common to take the difference between a risky bond (e.g., corporate bond) and a “risk-free” bond (e.g., a treasury bond).

•

Spot Curve1 The yield given by the spot curve is used to discount a single cash flow at a given maturity. The spot curve gives the YTM for zerocoupon bonds. The derivation procedure for the forward curve from the spot curve observes the no-arbitrage condition.

 Bank of England, Notes on the Bank of England UK Yield Curves, https://www.bankofengland. co.uk/-/media/boe/files/statistics/yield-curves/yield-curve-terminology-andconcepts.pdf?la=en&hash=FB7E974604FAE37155E0E649C70B2F2AF3FDD4CF, Zero-coupon yield curves: technical documentation, https://www.bis.org/publ/bppdf/bispap25.pdf

1

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Discount and Forward Curves1 A discount curve is used in the valuation of deterministic cash flows, and a forward curve is used for the valuation of linear payoffs.

The following are relevant criteria for assessing curve construction and its interpolation method: •

Is the data on the forward curve continuous and stable? The degree of stability can be quantified by looking for the maximum basis-point change in the forward curve given some basis-point change in a given input. Continuity is required as the pricing of some instruments is sensitive to the stability of forward rates.

•

What is the quality of the forward rates for a yield curve?

•

How local is the interpolation method?

•

How effective are the hedges?

•

How does delta risk get reflected in the curve?

Table 2-8 provides an overview of the market data used in constructing FX Curves.

Table 2-8.  Data for FX Curve Data Item – Source System

Risk System – Data Requirement

Risk Management System

Name Identifier Curve type Interpolate time Interpolate term Curve unit Surface Other risk data items Curve function & parameters

USD discount USD discount Zero curve Forward rate Linear %Semi/Actual/365 Term/Time – 30,90,180,1y,2y,5y,7y,10y,

Market & credit risk management system Mapped target data item

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Table 2-9 provides the an overview of the market data used in constructing a Interest rate curve.

Table 2-9.  Data for Interest Rate Curve Data Item – Source System

Risk System – Data Requirement

Risk Management System

Name Identifier Curve type Interpolate time Interpolate term Curve unit Other risk data items Curve function & parameters

IR USD – Bootstrap IR USD – Bootstrap Zero curve Forward rate Linear % CONT Act/365

Market & credit risk management system Mapped target data item

Forward volatility curves and forward-forward volatility curves are the two most commonly used curves for caps and floors. The latter ensures value additivity and is analogous to a zero-coupon interest rate curve.

C  urve Data Curves can be used to define term structures and risk factors such as indices and volatility surfaces. A curve consists of a start time and a list of points over time. A market risk management solution uses the curves for calculating risk sensitivities, pricing the financial instruments, setting up scenarios, stress testing, and optimization. The following are the classifications and data elements: a) Historic Rates Curve •

Name, date, relative curve (true/false), interpolate time, extrapolate time, curve unit (e.g., %Semi A/365), surface (time)

b) Yield Curve •

ID, name, date, relative curve (true/false), procedural parameters (e.g., Sept.30/99)

•

Functional parameters, function ID, time evolution (e.g., forward rate)

•

Interpolate term (e.g., linear), extrapolate term (e.g., false), curve unit, surface (e.g., term) 99

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c) Zero Curve or Index Curve •

Name, ID, date, relative curve, curve function, function identity, time evolution (e.g., constant)

•

Interpolate term, extrapolate term, curve unit, surface

d) Volatility Curve Surfaces – Volatility Term Curve •

Name, ID, date

•

Relative curve

•

Interpolate option term, interpolate moneyness, extrapolate option term

•

Extrapolate moneyness, surface

Table 2-10 provides the data dimension of a Volatility Time Curve.

Table 2-10.  Volatility Time Curve(Similar to Term Curve; Difference Is the Surface) Surface - Term

90

180

1y

.12

.09

2y

.14

.125

Bootstrapping Curves Bootstrapping is a process in which the curve construct is started with known data points and solved for unknown data points using an underlying arbitrage theory. There are three approaches to bootstrapping curves: (i). bootstrapping from instruments (ii). using the spread curve (iii). bootstrapping inflation expectation A coupon bond can be valued as a package of zero-coupon bonds that mimics its cash flows and risk characteristics. Zero-coupon bonds have a single payment at maturity. A theoretical zero-rate curve is constructed by mapping yields-to-maturity for each theoretical zero-coupon bond over the investment period. 100

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A zero curve can be understood as a yield curve that maps interest rates on zerocoupon bonds to different maturities across time. These are constructed for government securities (G Secs) and for interbank markets. Traders and risk analysts can use it to price financial instruments. Inconsistencies between the interbank curve and treasury curve can arise for the following reasons: (a) data provider’s mistake (b) collection of interbank and treasury data at different points in time (c) an impact of a temporary liquidity premium in the market

Missing Market Data A bank can use its proprietary model to fill in the missing data. For example, if the term structure for an IR curve is unavailable, the bank could (i) create a proxy IR curve that is a spread over some driving reference curve or (ii) generate a curve based on spot and forward FX rates. Missing data is also solved using one or more of the following approaches: •

A “fill procedure,” which uses •

the previous value before the missing data; or

•

the next good value after the missing data

•

Linear interpolation

•

Geometric interpolation

There are three main steps in bootstrapping: (i) instrument selection; (ii) determining cash flows; and (iii) zero-curve construction. The procedure can be understood as the construction of a theoretical spot rate curve, also referred to as a zerocoupon yield curve, from the prices of cash-flow-producing instruments. The curve is based upon the market prices of several underlying instruments. The curve will need to be calibrated to the market values. Table 2-11 illustrates the creation of a zero curve. The cash flows for a single instrument, and the market value, are known. The discount factor is unknown, and there is a factor for each time a cash flow occurs for each instrument. The matrix works well when the number of independent instruments is equal to the number of discount factors to be calculated (m=n).

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Table 2-11.  Equation to Create Zero Curve for Bootstrapping

The advantages of using zero curves instead of yield curves include the following: •

They are consistent with the “no arbitrage” framework.

•

They provide a unified valuation framework across all instrument types.

•

They form the basis for VaR methodologies.

•

They are useful for instrument pricing functions.

Curve bootstrapping is an important function in risk management solution implementation as it calibrates instruments’ curves to market data. Yield and zero curves may appear to be close, and the pricing difference may not appear significant at the instrument level; e.g., a bond level. However, the difference could become significant when derivative contracts use a bond as the underlying instrument.

Calibration Calibration eliminates the pricing difference between quoted market price and theoretically based valuation, given a set of market risk data. The theoretical model is adjusted to produce the market price. The need for calibration is established by the following factors:

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•

Uncalibrated models are likely to violate the no-arbitrage condition and can produce inaccurate results.

•

Instrument-pricing assumptions are invalid without calibration.

•

The true value of a portfolio is difficult to establish, and the error in the portfolio value will be large if it includes derivatives.

•

The inaccuracies are also reflected in the sensitivity value and VaR.

•

There are two common calibration techniques: •

parallel movement of an underlying curve (e.g., volatility, yield, treasury); and

•

absolute shift of an observed price.

Calibration of bonds is based on the implied spread that measures the parallel shift that must be made to the discount curve in order to make the theoretical price equal to the market price. The accrued interest is calculated to arrive at the dirty price of the bond. The theoretical value is calculated from the cash flows and discount curve. The spread is calculated and added to each node of the discount curve to make the theoretical value equal to the market value. By using the discount curve and the implied spread to price the bond, the valuation of the bond at future dates is more accurate. Bond futures are calibrated using a net basis. Calibration of forex forwards is based on interest rate parity, and an adjustment is made to the discount curve to make the theoretical model produce the market price. The inputs to generate the spread curve are the risk -free interest rate curves of the domestic currency and the foreign currency. The calibration of the FX instrument uses the foreign currency risk-free curve and the spread curve. For the calibration of options, the market price of the option, market price of the underlying, and the risk-free curve are required as inputs. Implied volatility is calculated and mapped to the surface.

Regression analysis is used to find the “best fit” line or curve for a series of data points. Chapter 7, Section 7.1.1.4 explains curves in the context of risk data characteristics and risk data ontology.

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2.1.1.2c  Portfolio Modeling The portfolio is “rolled-up” from the underlying instruments. The most important aspect of portfolio modeling is the definition of portfolio trees. Banks generally have a backward-pass validation to ensure that the structure defined in the compliance report is consistent with the portfolio tree definition in the system. The portfolio structure defined in the risk management system should also be consistent with the bank’s chart of accounts. Risk management systems provide flexibility in defining portfolio trees. Portfolio aggregation is the grouping of instruments or positions. This book is not on statistical modeling, and Figures 2-4a and 2-4b are provided so as to illustrate linearity and non-linearity of data distributions. Chapter 8, Section 8.1.2 elaborates on different types of data distributions and the approach for selecting a data distribution for a given set of losses.

Figure 2-4a.  Linear portfolio depiction

Figure 2-4b.  Non-linear portfolio depiction 104

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Linear Portfolio Simulation The model enables a risk manager to assess how a portfolio or exposure will behave under specified scenarios. It helps to “expect the unexpected” in an uncertain business environment. Banks use simulation for identifying and measuring the “known unknowns.”

2.1.2  Credit Risk in Treasury Books Credit risk in the context of enterprise risk–return management includes the counterparty risk in treasury instruments and borrower risks in corporate and retail loans. This section covers the credit risk in treasury books. It is a challenge to measure counterparty credit risk as it requires an assessment of the correlation between market and credit risk.

In a siloed architecture, credit risk in treasury contracts and credit risk in the loan books are measured and managed in two different systems. While some banks have rectified the situation with downstream integration, most banks continue with a siloed implementation. The downstream integration does not qualify as an enterprise approach, as it continues to be exposed to the risks caused by a fragmented architecture. Credit risk is the risk of loss that results from a counterparty’s failure to honor its contractual obligations. It involves the calculation of the size of the exposure, the probability of default assessment, and the potential loss assessment. The narrative in the previous pages on instruments, currency, and portfolios is relevant for credit risk management in the treasury books. However, credit risk has some specific features that need to be configured in the system, and the next section provides an explanation for these credit risk–specific tasks.

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2.1.2.1  Data Specific to Treasury’s Credit Risk Exposure Credit risk–related data attributes of treasury instruments include instrument identification, jurisdiction, seniority, counterparty, collateral and margin, haircut documentation, fees, drawdown, default calculation values, and cash flows. The market value of collateral has three components: market price, haircut rate, and accrued interest.

2.1.2.2  Financial Engineering – Modeling, Configuration The following credit risk management functions are configured in the risk management system: •

Instrument attribute mapping exposures, documentation, and jurisdiction

•

“Documentation” refers to netting agreement and includes (a) legal netting; (b) jurisdictions; (c) jurisdiction netting; (d) mitigation settlement; (e) mitigation trigger; (f ) mitigation amount; (g) paydown level for collateral management; (h) seniority class; and (i) risk limit.

•

Setting up recovery rates (RR)

•

“Legal entity” refers to the instrument and the counterparty. The attributes include credit rating, PD and RR of the counterparty, credit spread curve, collateral, margin call, and credit-to-close period.

•

Constructing the default probability curve and transition matrix curve

2.1.2.2a  Treasury Instruments Creating Credit Risk Exposure OTC derivatives and Securities Financing transactions are examples for counterparty credit risk exposure in treasury’s book.

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Additionally, certain types of settlement processes can create a counterparty risk. Credit risk management system configuration is part of the overall treasury risk management system configuration. The details in 2.1.2.2b and 2.1.2.2c are additional setup/configuration tasks that are specific to treasury instruments (2.1.2.2a) that create a counterparty credit risk exposure.

2.1.2.2b  Credit Risk Curve The curve is constructed using the credit parameter curve, credit spread curve, and probability of default (PD) curve, as detailed next. The rate transition matrix attributes are also attached to a curve in some credit risk management solutions. a) Credit parameter curve •

Name, ID, date

•

Relative curve

•

Surface (e.g., AAA, BB, B, default)

b) Credit spread curve •

Name, date

•

Relative curve

•

Interpolate term, extrapolate term

•

Curve unit (% A ACT/ACT)

•

Surface (rating & term–90/180/1y/2y)

Table 2-12 provides an example of data elements in a curve surface used for credit rating.

Table 2-12.  Curve Surface – Credit Rating Surface Term

AAA

AA

A

BBB

BB

B

Default

90

0.005

0.01

0.015

0.02

0.025

0.03

0.1

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c) Probability of default (PD) •

Name, date

•

Relative curve

•

Functional parameters

•

Interpolate time, extrapolate time

•

Surface

d) Transition matrix (is also modeled as a curve in some solutions) •

Name, date

•

Relative curve

•

Functional parameters

•

Surface

Table 2-13.  One-Year Transition Matrix Surface Term

AAA

AA

A

BBB

BB

B

Default

AAA

0.95

0.02

0.015

0.01

0.003

0.001

0.001

B

0.001

0.002

0.002

0.006

0.116

0.75

0.08

Default

0

0

0

0

0

0

1

Credit Value Adjustment (CVA) BCBS 325 & 424 CVA is calculated for a bank’s total CVA portfolio, and the scope includes risk-reducing effects, such as netting, collateral arrangements, and certain offsetting hedges. This is mentioned as a gap in Chapter 3, Section 3.2.3, and explained in Chapter 7, Sections 7.1.5.3 under “CVA Taxonomy” (Figure 7-17) and Section 7.4.1.4 under “Risk Data Discovery.”

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2.1.2.2c  Credit Risk Modeling The following are important procedures: •

2

Current exposure, also referred to as actual exposure, represents the size of the exposure if default happens today.

•

2

Potential future exposure (PFE) is the difference between total exposure and actual exposure. “Add-ons” refers to the safety margin and takes into consideration market price volatility.

PFE scenario simulation considers •

the changes to the current exposure over time based upon userdefined interest and exchange -rate scenarios;

•

credit risk mitigation; and

•

recovery adjustments to data points in the simulation point.

The probability of default and exposure of default functions are explained in Section 2.2.1.4.

2.1.3  Treasury Market and Credit Risk Measurement The data required for the numerical modeling includes prices, FX rates, interest rates, indices, volatilities, yield curves, covariance (scalar volatilities, vector volatilities, correlations), counterparties (counterparty hierarchy, ratings), and recovery rates. The following risk measures and models are explained further in the indicated sections: i. Mark to market – Section 2.1.3.1 ii. Sensitivities – Section 2.1.3.2 •

Scenario based

•

Analytical

 The standardized approach for measuring counterparty credit risk exposures, https://www. bis.org/publ/bcbs279.pdf

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iii. Value at Risk – Section 2.1.3.3 •

Risk metrics

•

Monte Carlo simulation

•

Historical simulation

iv. Stress testing – Section 2.1.3.4

2.1.3.1  Mark to Market (MtM) The risk management system allows a bank to mark a portfolio to the market at desired time intervals. This process reveals any changes to the risk exposure of the bank on account of these positions. Banks can monitor daily the unrealized profit and loss (P&L) and the sensitivity of the identified positions or portfolios. The mark-to-market value of a liquid position can be set to its fair market value. The risk appetite framework might require risk managers to set the value for all positions using a theoretically based model. This is required for the scenario-based sensitivity measures and risk profile calculations. When there is a difference between the mark-to-market value and the theoretical value, then the latter is calibrated. This is normally done using a spread shift or an appropriate factor for adjusting the theoretical value to the market price. Banks track the historic performance of a portfolio and identify the causes for the changes. The causes can be portfolio restructuring or market factors. The change can also be analyzed in terms of market risk factors. Attribution on account of a stated risk factor is measured as the change between start and end dates, with all other risk factors held constant. The core set of risk factors are (i) FX rates; (ii) interest rate curves-directional; curve change and shift in the time bucket; (iii) commodity price; (iv) equity price; (v) implied volatility; and (vi) time decay.

2.1.3.2  Sensitivity Analysis Sensitivities measure changes in portfolio value that follow changes in underlying risk factors. This comprises analytical or numerical calculations and scenario-based simulations. In numerical calculations, the underlying risk factor is shifted by a small amount and the instrument is revalued. An example would be PV01 for a bond. The analytical calculations at the individual position or at the portfolio level are delta, gamma, theta, vega, rho (bonds), monetary duration, and monetary convexity. 110

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The preceding measures (a) consider first- and second-order value changes; (b) do not consider asymmetric changes; and (c) cannot be applied across all products and instruments.

2.1.3.2a  Template for Risk Measure Data Table 2-14 mentions the sensitivity measurement approaches. In the as-is environment, most of these functions are available in the treasury management system and the market risk management system.

Table 2-14.  Sensitivity Measurement Approach Analytical or Scenario-based

Risk Analyst / Manager’s Comments

Analytical Delta, Gamma, Theta, Vega, Rho, Duration, Convexity Scenario-Based PV01, Bucket Scenarios

Please Note: This is an example for Overlapping functions between front-office treasury management (trading) system and the treasury’s risk management system. Risk governance provides guidance on the usage.

The mapping is linear except when there is an options portfolio. The risk factor sensitivities are the coefficients of the risk factor returns in the representation. The risk factor returns can be measured in either nominal or relative terms. Interest rate and volatility risk factor changes are taken as nominal changes, but changes in equity indices, exchange rates, and commodity futures are usually measured in relative terms; i.e.; percentage or log returns are used. Risk factor sensitivities may also be calculated in nominal or relative terms. An equity beta is a sensitivity that is often expressed in relative terms, whereas the present value of a basis point (PV01) is measured in nominal terms. It is simple to convert a relative sensitivity measure into nominal terms.

2.1.3.3  Value at Risk (VaR)3 “Value at risk” (VaR) is defined as the value that the bank can lose during an observed time horizon with a certain confidential level (probability level). Banks use it as a (a) measure that determines the amount of capital to be set aside to cover potential losses; (b) tool to allocate capital efficiently; and (c) method to estimate risk-adjusted performance.  An internal model-based approach to market risk capital requirements, https://www.bis.org/ publ/bcbs17.pdf

3

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The VaR statistic has a currency, a time period, a confidence level, and a loss amount/loss percentage. It provides insight into a bank’s risk exposure. The changing, non-linear composition of the trading portfolio necessitates the computation of incremental VaR (for a trade) and daily VaR. This is part of pre-deal analytics. The VaR calculation involves assumptions regarding distribution of future changes in the market risk factor levels during the stated interval and is based on the estimation of the distribution of future changes on MtM values over the stated time interval. Most banks assume that past distributions of risk factors are representative of a future distribution of risk factors. The following factors influence the selection of the VaR calculation approach: (a) distribution assumption with respect to market risk factor and MtM changes; (b) data availability, including data on non-linear changes; and (c) time for processing and resource requirements. Credit VaR models can be categorized as (i) default models (DM) and (ii) mark-tomarket (MtM) models. The former uses a binomial approach to identify defaults. There are two possible events: default or survival. The latter includes all possible changes of the borrower’s creditworthiness and is called credit migration. MtM models are multinomial as losses can be incurred when negative credit migrations happen.

Section 2.2.1.4 (vii) of this chapter explains credit risk VaR. Credit VaR models are built around the construction of a statistical distribution of potential credit losses. (Note: Chapter 8, Section 8.1.2.2 provides an example of selecting a data distribution for fitting for credit losses). These models are built on estimates that include the probability of default, the loss given default, and the exposure at default for individual credit exposures. Assumptions about the relationships between exposures (i.e., the correlations between them) are also key drivers of these models. Credit VaR models often assume a one-year horizon for assessing the potential for losses. The three standard VaR approaches are: (i) RiskMetrics - This book’s focus is on the covariance matrix approach (ii) Scenario-based Monte Carlo simulation (iii) Scenario-based historical simulation 112

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RiskMetrics4,5 Extending from Chapter 1, Section 1.4.1, the risk model provides a free data set that comprises volatility and correlation information for about twenty markets. This approach uses a variance–covariance method for calculating the VaR. Figure 2-5 illustrates the usage of the approach. The following are the core assumptions: •

Historic data can be used for predicting the future.

•

The distribution of portfolio returns and distribution of changes in market risk factors are multivariate normal.

•

The delta of the portfolio is constant.

•

Market risk can be adequately assessed by a relatively small number of risk factors.

•

In practice, the normality assumption for the distribution of changes in portfolio values does not work well for instruments with asymmetric payoffs. Linear approximation of change in value has proved to be incorrect for non-linear securities like options.

Covariance Matrix4 Covariance data comprise correlation and volatility files, and the data frequency is daily and monthly. RiskMetrics data contains information on equity, swap zero, money market, FX, and government zero for each currency. A volatility file contains predetermined risk factors for financial instruments and reflects the changes in price and yield volatility on a daily basis. The correlation file contains data on currencies and commodities. The following are the main steps in calculating RiskMetrics VaR4,5: (i) Transform and configure the covariance data in the risk management solution. (ii) Set parameter values.

 J.P.Morgan/Reuters RiskMetricsTM—Technical Document, https://www.msci.com/document s/10199/5915b101-4206-4ba0-aee2-3449d5c7e95a 5  Return to RiskMetrics: The Evolution of a Standard - https://www.msci.com/documents/10199/ dbb975aa-5dc2-4441-aa2d-ae34ab5f0945 4

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(iii) Attach relevant curves. (iv) Create a RiskMetrics model that would comprise portfolios. (v) Set VaR map attributes and vector calculations.

Figure 2-5.  Covariance matrix The 4RiskMetrics model supports the following VaR attributes and the associated pricing functions: (i) IR VaR – interest rate risk (ii) FX VaR - currency risk (iii) EQVaR – equity risk (iv) Relevant two-factor portion of RiskMetrics VaR a) FX-EQ VaR b) IR-EQ VaR c) IR-FX VaR (v) Curve VaR – non-parallel shift in the interest rate curve (vi) Direction VaR – parallel shift in the interest rate curve (vii) Spread VaR – spread between two or more IR curves

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(viii) Treasury VaR – portion of RiskMetrics VaR attributable to interest rate risk (ix) RiskMetrics VaR – standard deviations (x) RiskMetrics VaR – variance (xi) RiskMetrics mean value change calculates the annual mean value change for equities that are priced by the capital asset pricing model (CAPM) pricing function. (xii) RiskMetrics-specific VaR calculates specific risk of equities that are priced by the CAPM pricing function.

The preceding list provides insight into risk data types. This is further explained in the context of data ontology in Chapter 7 and risk data characteristics in Chapter 8.

Scenario-based Monte Carlo Simulation4,5 This approach involves the calculation of changes in the portfolio value for each defined scenario. A distribution of value changes is obtained from the calculation, and VaR is calculated by selecting the value at the preferred percentile from the cumulative distribution of value changes. The approach offers risk managers a recalculated position, including positions for non-linear instruments, under each defined scenario. VaR is calculated for non-normal distribution of risk factors. In a Monte Carlo simulation,3,5 values are sampled at random from the input probability distributions. It uses a pseudo-random number generator to simulate the random process so as to create thousands of hypothetical future scenarios based on probability. Using volatility and correlation data, the simulation produces scenarios by randomly selecting curves and maintaining the model’s correlation properties Extreme situations fall under the category of black swan events, and these stressed scenarios are part of the scope of the simulation. It requires the selection of parameters (estimates from historic data) and stochastic processes; e.g., log-normal processes. A scenario set uses a comprehensive list of risk factors in a variance–covariance matrix. Joint normality of percentage changes in risk factors is assumed, but there is no assumption regarding the distribution of changes in the MtM values. The risk management solution allows a bank to configure risk control settings such as the 115

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downside floor and upside target to shape the possible outcome. This is called outcome shaping. VaR is calculated as the non-parametric confidence interval for the realized sample distribution. Monte Carlo produces risk factor values at a single point in time only. The multi-step Monte Carlo enables a risk analyst to add individual mean reversion coefficients for each generated simulation curve. It uses a mean-reversion method to produce risk factor values through time. A Monte Carlo simulation invokes several hundreds or thousands of iterations, and the output is a probability distribution of possible outcomes. The following are the most common scenario generation methods:4,5 •

Bucket scenario uses curve-variable functions, a non-parallel shift, and a variable factor. A shift is specified and applied sequentially for each term point on the curve.

•

Hull-White Monte Carlo generates scenarios on one or more probability factor(s). Each scenario is a path of all relevant risk factors over time. The “Hull and White” parameters are linked to volatility data for mean reversion coefficient calculations.

•

A procedure to generate normal distributed scenarios

Scenario Generation The following are curve requirements and data attributes: •

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Parameter Curve •

ID, name , date, relative curve

•

Functional parameters, function identity

•

Interpolate axis-1 (linear) time/term axis, interpolate axis-2 (linear) time/term axis

•

Default interpolation

•

Curve unit (% Semi A/A)

•

Surface

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•

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Index Factor Curve •

Name, ID, date, relative curve

•

Curve function

•

Function identity

•

Time evolution (e.g., sliding axis)

•

Interpolate time (e.g., linear), extrapolate time

•

Surface (factor value at a point in time)

Return Factor Curve •

Name, ID, date, relative curve

•

Curve function

•

Function identity

•

Time evolution (e.g., sliding axis)

•

Interpolate time (e.g., linear), extrapolate time

•

Curve unit (e.g., %Annual A/365)

•

Surface (factor value at a point in time)

The following are components of a general framework for portfolio credit risk measurement: (i). Risk factors and scenarios (ii). Joint default/migration model (iii). Obligor exposures, recoveries, and losses in a scenario (iv). Conditional portfolio loss distribution in a scenario (v). Aggregation of losses in all scenarios

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Scenario Data The scenario attributes are similar to the instrument attributes. A scenario set has a number of individual scenarios: •

Scenario set ID, scenario set name

•

Weight or probability

•

Type (absolute, constant factor, variable)

•

Scenario probability

•

Variable (e.g., USD yield, GBP FWD)

•

Functional parameters

•

Time evolution to trigger – specifies curve evolution

•

Time evolution from trigger

•

Rules attached

•

Value (floating point or a sub-matrix indicating a curve structure)

In a scenario definition, the variables available for selection are instrument position and a curve. The type of shift can have the following values: •

Constant = Curve node * Constant factor

•

Parallel shift = Curve node + Constant value

•

Variable factor = Curve node * relevant factor

•

Non-parallel shift = Curve node + differing variable factor

Table 2-15 provides the template headings for a scenario set.

Table 2-15.  Scenario Set Set Name

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Scenario Name

Scenario Variable Time Evolution to Probability Trigger

Time Evolution from Trigger

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In a scenario-based solution, unit changes to market risk factors are expressed as scenarios in stress testing. The approach fully re-prices for a unit market risk change, and all orders of change in value are taken into account. Asymmetrical values can be specified, and the application of a risk factor is not restricted to an instrument. Examples: •

Upward shock to implied volatility surfaces for a specified product/ list of products

•

Appreciation of a spot FX rate with respect to a reference currency

•

Upward basis point shifts to specified nodes of individual interest rate curves

Table 2-16.  Mark to Market Consideration

Risk Analyst / Manager’s Comments

Frequency Calibration

Table 2-17.  Value at Risk Approach

Time Horizon

Confidence Interval

Risk Analyst / Manager’s Comments

RiskMetrics Monte Carlo Historical Monte Carlo simulation provides a number of advantages over deterministic analysis. The following are the comparative strengths: •

All the delta and gamma effects are incorporated in the VaR measure.

•

The sensitivity of each input to the output can be understood.

•

It provides the probability for each outcome.

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•

It is possible to model interdependent relationships between input variables.

•

Data visualization of the output makes it easy to understand and make decisions.

Historical Simulation4,5 This approach is similar to Monte Carlo. It does not use a variance–covariance matrix, and generates a set of scenarios from past realizations of risk factor changes, over a given period. Availability of good-quality historical risk factor data is critical for this approach. Figure 2-6 illustrates common elements for scenario-based Monte Carlo simulation and scenario-based historical simulation approaches. The figure is referred to again in the next section (2.1.3.4) on stress testing.

Figure 2-6.  Risk simulation components

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Marginal VaR, Component VaR, Incremental VaR5 •

The marginal VaR5 measures the sensitivity of portfolio VaR to a small change in an asset holding.

•

Component VaR5 is the contribution of a specific position to the entire portfolio’s VaR. The following are the main components of VaR: •

Interest rate represents positions whose values are sensitive to interest rate volatility.

•

FX represents positions whose values are sensitive to exchange rate and/or interest rate volatility.

•

The equity component of VaR represents the risk exposure on domestic and foreign stocks and other equity-linked instruments. These include exchange-traded funds and OTC instruments.

•

Incremental VaR5 is used to measure the change in a portfolio’s VaR as implied by a change in a position. It recomputes the entire portfolio value at risk, both before and after a given change is made. It measures the incremental effect on VaR of adding a new exposure to an existing portfolio.

Value-at-Risk derives a single quantitative measure of the potential for losses over a specified time horizon by aggregating the components of price risk. This model exhibits the market risk of the entire portfolio in one number and focuses on, in currency terms, the loss of a portfolio value. This explains its popularity.

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Table 2-18.  Portfolio VaR – Aggregation of Risks

Stressed VaR Stressed VaR6 is the dollar amount of the potential loss at a specified confidence level from adverse and stressed market movements. Regulatory stressed VaR assumes a ten-day holding period, 99% confidence level, and a 250-day look-back with a weighting method. Tables 2-19a and 2-19b provide a comparison of the different VaR approaches.

 Interpretive issues with respect to the revisions to the market risk framework, https://www.bis. org/publ/bcbs193a.pdf

6

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Table 2-19a.  Comparison of VaR Approaches Feature

RiskMetrics / Parametric Monte Carlo (aka Variance– Simulation Covariance)

Historical Simulation

Distribution Assumption Market Risk Factor

1. Future distribution of asset prices is reflective of past distributions 2. Joint normality of percentage changes in market risk factors

1. Future distribution of asset prices is reflective of past distributions. 2. No distribution assumption

–Distribution Assumption MtM Valuation

Normal distribution

No assumption

No assumption

Valuation

Delta equivalence to cash flows

Each scenario is revalued fully

Inputs

Variance–covariance matrix

Historic data for all risk factors

Table 2-19b.  Comparison of VaR Approaches

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VaR Limitations VaR continues to be the de facto risk measure in all banks because of its ability to cover linear and non-linear risk exposures, respond to the change in the composition of trading portfolios, and reflect the benefits from diversification. Its limitations are as follows: •

It is not always sub-additive. If VaR is calculated for each risk type, the sum of the values at risk of each type is not equal to the VaR of the whole bank. This makes it less effective as a measure for portfolio optimization and internal allocation of capital.

•

It can violate second-order stochastic dominance and may not highlight risk aversion.

•

It does not give any indication of the losses that exceed the VaR. It disregards any loss beyond the VaR level. This is called the tail risk.

Backtesting7 Backtesting is a model validation process that verifies to what extent actual losses match expected losses. It is a tool that risk managers apply to verify the accuracy of risk measures. Backtesting strategies include (a) frequency-based tests; (b) magnitude-based tests; (c) multivariate tests; (d) independence tests; and (e) duration-based tests. The popular methods are as follows: •

Unconditional methods count the number of exceptions and compare them with a confidence level. If the exceptions are within statistical limits, the model is accepted.

•

Conditional methods test whether the exceptions are independent of each other.

The statistical VaR backtesting methods include binomial, traffic light, Kupiec, Christoffersen, and Haas.

 Supervisory Framework for the Use of Backtesting in Conjunction with the Internal Models approach to Market Risk Capital Requirements, https://www.bis.org/publ/bcbs22.pdf

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2.1.3.4  Stress Testing This discussion is an extension of the section on scenario-based Monte Carlo simulation and scenario-based historical simulation. Figure 2-6 and 2-7 cover the dimensions. Enterprise stress testing is explained in Chapter 10. Stress testing is a risk management tool that helps banks assess the potential impact of an unknown risk or an unplanned event on the bank’s performance. It is a forwardlooking analytical tool that provides the ability to incorporate risk into the business planning processes, and it is an essential component for defining risk appetite. It works on “what if” scenarios that lead to a call for action. Actions may take the form of developing contingency plans, reducing concentrations, or changing the capital structure. The following are examples of stress-testing methods: •

Accounting-based approach is for stress testing the balance sheet

•

Market price–based approach includes equity indicators–based testing

•

Extreme value theory approach

•

The macro-financial approach

Figure 2-7.  Stress testing 125

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Figure 2-7 mentions the high-level milestones of a stress-testing framework. The objectives of the test determine the scenarios. In order to define scenarios, a risk analyst identifies risk events and factors that can generate a loss. The stress-testing exercise focuses on validating the bank’s risk capacity.

Scenario Definition Scenario definition is done using representative scenarios and crisis scenarios. A risk analyst performs a stress test by specifying alternate market scenarios and recomputes the portfolio value and sensitivities under each scenario. Representative scenarios are drawn from known market distributions of risk factor–level changes. This involves sampling from historical risk factor data and extracting worst-case observations over a specified time horizon. Examples would include •

parallel shifts, steepening, flattening, inversion of interest rate curves; and

•

flattening or steepening of volatility surfaces.

Analysis of a bank’s risk profile is incomplete without stress testing its positions using a representative set of shock scenarios. A good stress test has a significant number of scenarios from crisis situations and do not reflect market distributions. These scenarios contain all key market risk factors and are tested for correlation breakdowns at the same time. The following are some examples of shocks (a) 1987 equity market crash (b) the 1992 ERM crisis and (c) risk factor level changes observed during the 2007-08 Sub-prime Mortgage crisis and the consequential global meltdown.

Scenario Types There are two types of events for stress testing and scenario analysis: historical or hypothetical. The risk manager is guided by the bank’s policy for scenario selection. 5 (i) The data on historical scenarios include causation and the sensitivity of the portfolios to shocks. However, data are restricted to previous risk events. An estimate is made of what the maximum daily loss–generated rates are, and prices are applied to the current position; the impact on the bank’s portfolios is measured. 5 (ii) Hypothetical scenarios do not replicate past incidents, and the events are systemic, albeit without precedence. They tend to have scenarios that are probable and most adverse. In this method, the ranges of fluctuation of certain specified risk factors 126

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are determined and configured. The maximum loss is generated on a daily basis when these adverse factors are applied to the current position to ascertain the weaknesses in the portfolio.

Configuring Stress Tests Stress-test configuration has the following steps: i. Create scenario sets. ii. Specify portfolio(s) and attributes. iii. Specify the start and end dates for simulation.

Scenario Sets A scenario set includes scenarios that consist of expected one-day changes in all relevant factors. It would also include a no-change scenario for comparison purposes. The probability can be set at a low value for this. Scenarios are created for interest rate, FX, volatility, equity, and index risk factors. By adding all these scenarios to a set, the effect of the changes for all risk factors can be attributed to changes in individual types of risk factors.

Portfolio Selection This is consistent with stress-testing needs; the risk analyst defines the aggregation scheme. For banks with large portfolios, risk management solutions allow the risk analyst to consolidate multiple instruments into a smaller set of instruments that retain the fundamental properties of the original instruments. This provides faster processing capabilities. A direct impact on the processing time is created by the number of (a) scenarios in a set; (b) positions in the portfolio; (c) attributes; and (d) simulation-points in time. A simulation, over time, •

assumes that all changes to risk factors occur at the start date. Timetriggered scenarios can work differently; and

•

reflects the time-decay effects.

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Market Risk Stress-Test Approach

As explained in Section 2.1.1, market risk comprises interest rate, exchange rate, stock risks, and commodity risks. The scope of the stress test will include a range of plausible market shocks that affect rates, prices, market liquidity, funding liquidity, and defaults. The stress-test scenarios definition creates significant shifts in correlations and volatility. The objective is to test the impact of the shocks on Available for Sale (AFS) and Held for Trading (HFT) instruments, illiquid positions, non-linear cash flows, deep out-of-themoney positions, and jumps to default. 5

Treasury – Credit Risk Stress Testing

Table 2-20 provides a view of the salient aspects of credit risk stress testing.

Table 2-20.  Treasury – Credit Risk Stress Testing

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2.1.3.5  Credit Risk Reduction Techniques Limits are set based on risk appetite. Credit risk mitigation techniques include the following: i. Collateral minimizes the risk exposure. In certain types of exposure, collateral is called only if a limit is exceeded or when an event happens; e.g., ratings downgrade. ii. Limits can be defined for a counterparty, credit risk grade, geography, sector, or based on tenor. iii. For derivatives, the master agreement (MA) specifies the netting modalities. Examples of modalities include netting all transactions or only a restricted set of transactions to obtain the net exposure. iv. “Recouponing” refers to a change in the coupon of a bond or a swap rate. It is useful in weak collateral enforcement environment jurisdictions. v. Credit default swaps vi. Options on credit spreads

Credit Derivatives Credit derivatives enable a bank to minimize risks associated with the counterparty trades. The instrument transfers credit risk linked to an underlying entity from one party to another without transferring the underlying entity. The common types of credit derivatives are credit default swaps, credit default index swaps, collateralized debt obligations (CDOs), total return swaps, credit-linked notes, asset swaps, credit default swap options, credit default index swaps options, and credit spread forwards/options. The most popular CDS is the credit default swap.

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Credit Default Swaps (CDS)8 In a CDS, the seller agrees, for an upfront or continuing fee, to compensate the buyer when a specified event occurs. CDS are composed of the following four types: (a) on single entities; (b) on a basket of entities; (c) credit default index swaps; and (d) first-loss and tranche-loss credit default swaps. The value of a default swap depends on the probability of counterparty default, probability of entity default, and the correlation between them.

In the as-is environment there is an overlap between the treasury management system (TMS) functions explained in Chapter 1, Section 1.3.1.1, and the risk management functions explained in this chapter, from Section 2.1.1 to Section 2.1.4. In production, risk management processes are triggered in the (a) “source” TMS; (b) in the middle office treasury’s risk management system; or (c) processed by the risk management system, and the results are made available to the front-office TMS via an API. This is further explained in Chapter 3, Section 3.1.2.2, in the context of identifying gaps between the as-is and target environments.

2.1.4  Performance Attribution The following are the characteristics of an attribution model: •

Computation is bottom up; interpretation is top down

•

Captures all trades and revisions

•

The performance measurement is consistent with key risk factors, market conditions, and investment strategy.

•

Accommodates new products/asset types

 Credit Default Swap: https://www.ecb.europa.eu/pub/pdf/other/creditdefaultswapsand counterpartyrisk2009en.pdf

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For the treasury, banks use three forms of attribution (a) multi-factor analysis; (b) style analysis; and (c) return decomposition analysis. The salient features of each are as follows: •

•

•

Multi-Factor Analysis •

These include factors such as bond durations, EVE, and NII ratios

•

Requires economic (external) accounting and risk data

Style Analysis •

Easy to calculate and explain; an example is the Sharpe ratio

•

Return Decomposition Analysis

Popular, as it is easy to calculate, understand and explain •

Focuses on portfolio selection and allocation

•

Portfolio performance vs. benchmarks

Sharpe, Treynor, Sortino, and Jensen are performance measures. Sharpe and Treynor are similar, and risk premium is a common element in both. Treynor is used for diversified portfolios. Sortino is a modification of the Sharpe ratio and uses minimumacceptable return and semi-standard deviation. This is used as a measure when returns are asymmetric. Jensen measures an asset’s excess return over the capital asset pricing model’s predicted return.

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Figure 2-8.  Performance attribution model – fixed income & equity With reference to Figure 2-8, the following factors can be used to measure fixed income and equity attribution: •

FX effect

•

Yield curve and duration effect

•

Allocation effect as in overweights’ / underweights’ impact on payoffs

•

Selection effect

Chapter 7, Section 7.1.3 explains enterprise risk and performance taxonomy, and Section 7.3.2 extends this narrative to enterprise risk-adjusted performance metrics.

2.2  Credit Risk in the Loan Book Credit Risk in the loan book can be understood from the perspective of the loan cycle and the quantification of risk.

2.2.1  Risk Perspective of the Lending Process Risk management tasks are performed at every stage of the credit life cycle. Starting from the loan application, through negotiations, documentation, disbursement, monitoring (restructuring if warranted during life cycle), collateral management, and collections, to the last stage of loan closure. 132

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Figure 2-9 is a simple, high-level depiction of an end-to-end loan cycle in a bank. Market risk factors like interest rates can cause credit risk. Non-performing loans can create a liquidity risk. Many lending processes have an exposure to operational risk. Chapter 5, Section 5.6.3.3 provides several examples of lending process automation and credit risk management.

Figure 2-9.  Credit risk – corporate lending process

2.2.1.1  Internal Credit Rating System The CAMEL method uses the following six factors to make an assessment: (i) Capital; (ii) Asset quality; (iii) Management quality; (iv) Earnings; (v) Liquidity; and (vi) Sensitivity. •

Capital indicates the relative balance sheet strength of an organization. An important aspect of it is its leverage and the mix of debt and equity.

•

Asset quality is one of the core components of the bank’s financial health.

•

Management quality is reflected in the corporate governance model.

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Earning is the most important performance measurement of banks.

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Liquidity risk measures an institution’s ability to meet its expected and unexpected requirement of funds.

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Sensitivity to external and internal risk factors is a key factor. 133

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Credit scoring models use a combination of financial and non-financial factors. Most banks use the CAMELS rating method, which is available in many credit risk management solutions. Scoring is another overlapping area between front- and middleoffice functions in the siloed environment.

Obligor and Facility Rating9 Corporate lending risk rating systems have a two-dimensional design that includes (a) an obligor rating that is based on the borrower’s default probability and (b) a facility rating that takes into account characteristics of the loan (structure, purpose, usage) and its impact on recovery and default. This would include the complexity of the product (e.g., syndicate lending, jurisdiction issues), cash-flow patterns, collateral characteristics, and contract stipulations. Most risk management systems have sufficient parameters that allow a bank to configure the risk profiles adequately. 9 In order to assign obligor ratings, banks consider the following data categories and their relationships (see Table 2-21): •

Ownership structure (details of shareholding pattern, directors, group companies)

•

Financial health – Growth and profitability, balance sheet leverage, profitability, cash flows

•

Analysis of business sector, growth prospects of the sector

•

Country risk and external ratings

 The Internal Ratings-Based Approach, https://www.bis.org/publ/bcbsca05.pdf

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Data entities and relationships for facility ratings include the following: •

Facility requirements – Nature and purpose (intended use) of loan, loan structure, legal and regulatory requirements

•

Collateral – Liquidity, market value, and legal enforceability

The industry best practice for obligor ratings is to have at least nine credit-risk grades for non-defaulted borrowers and three for defaulted borrowers. Facility ratings have at least six grades. The bank’s credit risk management policy should provide guidance on the calibration methodology for differentiating the credit quality of two consecutive grades. Most rating systems are compliant with the following common regulatory (not specific to any country) requirements: •

Borrowers are rated or reviewed at least once a year and reviewed when new information about the borrower comes to light.

•

Risk factors are assessed for a future horizon based on current information, and grades are assigned accordingly to borrowers.

•

Each grade has a probability of default for the following year.

Table 2-21.  Example for Facility Grades and Obligor Rating9

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Retail Lending – Individual Credit ratings for individuals include personal financial factors such as personal income, wealth, and debt burden. The time horizon for probability of default estimation is one year, but banks use a longer time horizon while assigning ratings. FICO is an example of a personal credit rating score. The FICO Credit Capacity Index goes beyond traditional income-based measures of assessing consumer debt capacity and uses a patented analytic approach. The FICO Economic Impact Index can help banks understand how future economic fluctuations may affect a consumer’s “Equifax RN4” credit score.

2.2.1.2  Credit Monitoring Portfolio Composition Effective management of a loan portfolio requires having information on its composition and inherent risks. “Portfolio composition” refers to the product mix, customer segments, industry and geography, concentrations, ratings, and other aggregate characteristics. Figure 2-10 illustrates the setting of concentration limits for different sectors. The proactive monitoring of these limits is an important way to minimize concentration-risk on a sector (e.g., real estate).

Identifying Concentrations of Risk9 Credit risk managers group portfolios into pools of loans with similar characteristics to evaluate them against the bank’s Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs). Examples of such groupings include by corporate entity, entity group, sector and geography. Concentration risks can be prevented at the credit appraisal stage, as the credit appraisal department can deny approvals for loans to sectors where concentration risk exists.

Managing concentration risks is a very important aspect of Enterprise Risk Management (ERM). This is explained further in Chapter 8, Section 8.1.7.

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Figure 2-10.  Lending – sector portfolio caps Transition matrices9 represent credit-quality migrations over a specified period of time (usually a year). The matrices are used in most credit risk modeling techniques. Many credit risk models use multivariate models that take into account the impact of business cycles. Transition matrices are unsuitable for products with a maturity date that is less than a year away. Several analytical reports have established that credit losses were incurred by banks because rating downgrades lagged behind the actual deterioration in credit quality. It is a best practice for banks to rely less on external ratings and have a standards-based, robust internal ratings model.

Validate with External Rating For corporate banking, a bank’s internal ratings could be validated against the scores of external agencies on a sampling basis. When an external rating differs significantly from the proposed internal rating, an investigation into the internal methodology may be warranted.

2.2.1.3  Loan Book Stress Testing Loan book stress testing has two focus areas: (a) macroeconomic factors and (b) concentration risk.

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Macroeconomic factors are usually very relevant for assessing future credit losses and for determining the adequacy of provisions for bad loans. It is important to include large exposures and identify concentration risk. Loan book stress tests can vary by asset types; for example, corporates, SMEs, individuals’ mortgages, individuals’ credit cards, and consumer finance. This distinction helps in defining better scenarios for stress testing. A bank carries more credit risk on its corporate loan book than on retail lending. The risk appetite framework provides guidance on the types, size, and number of shocks that would need to be administered. The stress test assessment considers their impact on the different items of the profit and loss account and the expected loss (EL). The impact on EL is obtained from the estimated change in the Probability of Default (PD) and Loss Given Default (LGD).

2.2.1.4  Credit Risk Management Approaches Chapter 1, Section 1.4 provides an overview of the evolution of the BASEL risk management recommendations. The BASEL recommendation also includes credit risk in the treasury books. The standardized approach (SA)10 classifies credit exposures in distinct categories based on the risk of borrowers, either as measured by external ratings or as determined by the type of exposure. Many banks use external credit rating agency assessments for determining the risk weights used in the calculation of capital requirements. Table 2-22 provides an example of rating-based risk weights in the standardized approach.

Table 2-22.  Risk Weight for Credit Risk Management – Standardized Approach

 The New Basel Capital Accord, https://www.bis.org/bcbs/bcbscp3.pdf

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The summary of risk-weight categories in the standardized approach is as follows: •

10

•

10

•

10

•

10

•

Other assets

Claims on sovereigns Claims on banks and securities companies Claims on corporates

Claims secured by residential property, claims secured by commercial real estate, overdue loans

For collateralized exposures,10 there are two methods of credit risk mitigation that may be used under the standardized approach: •

The simple method substitutes the risk weight of the collateral for the risk weight of the counterparty.

•

The comprehensive method takes into account the possible future changes in exposure and collateral by off-setting the collateral against exposures. It reduces the exposure by the adjusted value of the collateral, adjusted for possible future changes.

For banking book risk mitigation, either method may be used. However, if collateral is pledged against counterparty risk in the trading book, the comprehensive method must be used.

Definitions10 •

“Probability of default” (PD) is the likelihood that a borrower will default on debt obligations when they are due for payment.

•

“Exposure at default” (EAD) refers to the amount outstanding. It is expressed as a gross of partial write-offs and specific provisions. A credit conversion factor is used to calculate EAD for off–balance sheet items.

•

“Loss given default” (LGD) gives the percentage of exposure the bank might lose if the borrower defaults, and is calculated as a net of risk mitigants.

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The internal ratings-based (IRB) approach relies on the capability of banks to calculate their own credit risk parameters and use them for determining capital requirements. There are two sub-approaches within IRB, as follows: 11

The foundation approach (F-IRB) differs from the advanced approach on the number of own risk estimates that can be used to compute capital requirements. Many of the risk inputs are provided by the supervisor under the foundation approach. Under F-IRB, banks can use an internal estimate for PD but are required to use a regulator’s prescribed EAD and loss given default (LGD) for calculating the RWA (risk-weighted asset) for non-retail portfolios.

•

11

•

For retail exposures, banks use their own estimates of the IRB parameters (PD, LGD). The total required capital is calculated as a fixed percentage of the estimated RWA.

•

11

The advanced IRB approach (A-IRB) is more flexible and allows banks to evaluate various factors related to default, exposure, and different types of collateral. The advanced IRB approach relies on measures of borrower creditworthiness generated internally by the bank as primary inputs to the capital requirement calculation. Banks use their own quantitative models to estimate PD, EAD, LGD, and other parameters required for calculating the RWA (riskweighted asset). Then the total required capital is calculated as a fixed percentage of the estimated RWA. This reliance on internal risk inputs makes the A-IRB capital requirements more accurate.

Probability of Default (PD)10,11 PD only measures the ability to repay and not the willingness to pay (behavioral). In its application, PD is usually incorporated into a rating scale, with the best grade having the lowest PD and the worst grade having the highest PD. A rating represents the bank’s assessment of the borrower’s ability to discharge the contractual obligations even under adverse business conditions. These aspects are illustrated in Figure 2-11.

 The Internal Ratings-Based Approach, https://www.bis.org/publ/bcbsca05.pdf, An Explanatory Note on the Basel II IRB Risk Weight Functions, https://www.bis.org/bcbs/ irbriskweight.pdf

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Figure 2-11.  Probability of default

Probability of Default (PD) – Model Selection To measure PD, risk analysts use models such as linear probability models, logit models, probit models, and discriminant analysis models. Chapter 8, Section 8.1.4 covers discriminant analysis, and 8.1.8 covers regression analysis. Banks using advanced IRB provide valid estimates of PD for each borrower grade and for each asset class. Banks either (a) assign a PD to each exposure in a given asset class or place exposures into “buckets” of similarly graded credits; or (b) estimate a PD for each bucket, generally based upon at least five years of data.

Recovery Rate (RR) 10,11 Recovery rate (RR) is defined as a fixed ratio of the outstanding debt value and is therefore independent from the PD. Recovery rate = Amount collected – cost of collection. Depending on the types of facility and collateral, a bank estimates the recovery values from its own historical or market experience. Some risk management systems allow banks to run simulations of future what-if scenarios to arrive at these expected recovery rates.

The value recovery process (VRP) approach treats default recovery as a sequence of events. Event-driven architecture is covered in Chapter 5, Section 5.5.1; data streaming in Chapter 6, Section 6.2.3.5; data lineage, 6.2.7.3; and Chapter 7, Section 7.5 covers event-driven, data-centric enterprise risk management. 141

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Table 2-23.  Credit Risk Management-Probability of Default Models

Loss Given Default (LGD)10,11 LGD is the loss expected when a borrower defaults on its obligation, net of eligible collateral and risk mitigants. LGD is expressed as a percentage of the total exposure—i.e., outstanding loan amount at time of default. LGD is modeled using the different collateral types that support the loan contract. There are two approaches to credit risk analysis: i. Construction of a joint multivariate distribution of market risk factors (e.g., interest rate) and credit risk factors (e.g., default probability and recovery rates) at various points in time. This requires default data for using in correlation estimates. ii. The second approach assumes that there is no correlation between market and credit risk factors. Although this is understood as incorrect, many banks implement this approach. There are two conditions that need to be satisfied for credit loss recognition: (i) the no-default value of the contract must be positive and (ii) the counterparty must default.

LGD Models Models that derive LGD from historical data can understate the LGD estimates, as collateral values are correlated to economic cycles. A formula-based approach calculates LGD using collateral haircuts, recovery rates, administrative costs, and cost of carry. The approach provides for an adjustment factor that considers economic cycles. The following points are relevant for the estimation: 142

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•

Collaterals reduce LGD.

•

Liquidity of the collateral (normal and forced sale)

•

Type of charge

The factors in estimating costs and recovery in LGD modeling are (i) covered & uncovered (collateral) exposure and (ii) the discounting rate that can be applied to estimated recovery. Costs include administrative costs and costs linked to collections and asset sale.

Expected Loss (EL)10,11 When expressed as a percentage, EL = PD * LGD.

Exposure at Default (EAD)10,11 EAD is the loan amount outstanding at time of default. For off–balance sheet exposures, such as unused loan commitments, banks are required to apply credit conversion factors (CCFs) to the unused exposure amount in order to generate the EAD. The foundation IRB banks apply supervisor-supplied CCFs to unused commitments that are off– balance sheet. Banks using advanced IRB use their own CCF estimates to determine EAD. As in the case of LGD, the EAD estimate is based on a conservative estimate of the long-run average. Where EAD estimates are volatile over an economic cycle, a bank uses a more conservative economic downturn estimate. When expressed as a currency value, expected loss (using EAD) is computed as EL = PD * LGD * EAD.

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Table 2-24 provides a template for the calculation of Loss Given Default.

Table 2-24.  Calculation of Loss Given Default

Maturity (M)10,11 In the foundation IRB approach, the effective maturity is 2.5 years (except for repo agreements, which are six months). M is the greater of one year or the effective maturity of the specific instrument, but total M is capped at five years.

Some banks prefer duration rather than maturity as a risk factor, as it better reflects credit grades, business cycle, seasonal effects, and nuances of lending.

Figure 2-12.  Credit risk capital calculation11,12 The credit risk capital calculation for the three approaches is discussed next (see Figure 2-12).

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Calculation Approaches for Credit VaR Unexpected Loss (UL)10,11 Figure 2-13 illustrates unexpected loss (UL), expected loss, and extreme loss. UL is not anticipated, though it is predictable, but the bank is able to absorb it in the normal course of doing business. Extreme losses are rare, and the bank must be able to survive and remain solvent during such stressed conditions. The sum of the unexpected loss and the extreme losses is the amount that the bank has to set aside in order to maintain its solvency. The relationship between a loss level and its probability of occurrence is called the probability loss distribution. Credit VaR is a risk measure that is derived from the portfolio loss distribution. This is explained in Chapter 8, Section 8.1.2.2. Credit VaR12 measures the maximum loss on an asset or a portfolio over a given time at a specified confidence level (e.g., 99 percent). There are two ways to compute this measure. They are (a) parametric VaR and (b) Monte Carlo (MC) simulation–based VaR. The assumption made by the parametric credit VaR approach12 is that the economic capital depends on the standard deviation of the loss on each line of the portfolio and on the correlations between each line. The Monte Carlo method12 generates a loss distribution using Monte Carlo simulations. It finds the loss level corresponding to a specified confidence level. The following are the main Monte Carlo simulation steps. i. Estimate the defaults and losses of each borrower in the portfolio. The simulation assigns a rating and a loss given default to each borrower. ii. Estimate the dependence between borrowers; i.e., determine pairwise asset correlations. Some models additionally use industry and country correlations. iii. Generate the correlated defaults and loss given defaults, after accounting for collaterals. iv. Compute the losses at the transaction level and add them at the portfolio level. v. By repeating the preceding steps a large number of times, generate the loss distribution and determine the VaR.  Credit risk modeling, https://www.bis.org/publ/bcbs49.pdf

12

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Figure 2-13.  Expected and unexpected loss Table 2-25 provides a credit risk exposure template for calculating the capital charge using risk-weighted assets.

Table 2-25.  Credit exposures and risk-weighted assets

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Interest rate risk in the banking book (IRRBB) and Fundamental review of the trading book (FRTB) are covered in Chapters 3 and 7.

2.3  Asset Liability Management (ALM) This function is managed by the Asset Liability Management Committee (ALCO). The system is either owned by the chief financial officer or by the head of treasury, depending on the bank. Further, as explained in Chapter 1, Section 1.3.5.2, some asset liability management (ALM) systems include an FTP module.

2.3.1  ALM Overview This section on ALM provides an overview, followed by a look at the implementation of ALM in a bank.

Central Bank Operations and Their Impact on a Bank’s ALM In many countries, the central bank mandates that commercial banks hold a sufficient proportion of their assets in the form of “riskless” instruments with the central bank for monetary control purposes. Central banks change the level of minimum reserve requirements of commercial banks to influence the levels of liquidity in the economy and the price of short-term funds. These regulatory reserves are supplemented by the central bank’s open market operations (OMO). This is achieved through the buying and selling of short-term instruments in the market (Figure 2-14).

The central bank’s liquidity management measure has an impact on the commercial bank’s liquidity management.

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Figure 2-14.  Asset liability management overview, components If a central bank buys treasury bills, then the increase in demand will cause yields to fall. This influences the price of other instruments and has an impact on the bank’s portfolio mix of these assets. Further, it influences the interbank rate and has an impact on loan pricing and credit growth. Currency is the core component of an ALM model, as cash flows are managed by currency type. A bank’s balance sheet is exposed to exchange risk and interest rate risk simultaneously. Figure 2-15 illustrates these aspects.

Figure 2-15.  Asset liability management ecosystem

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Commercial Bank ALM Objectives The ALM program i. aims to create a dynamic capability to optimize its capital structure by providing insights into the adequacy of its capital for potential future earnings on a regular basis. Banks strive to have a lower capital-to-assets ratio to enhance the return on capital employed; ii. monitors the sensitivity of the balance sheet and earnings to interest rate changes; iii. manages the cash flows; iv. immunizes and optimizes the balance sheet; immunization and optimization are dependent on an effective interest rate risk management framework; v. supports efforts to accomplish the net interest income and net interest margin (NII & NIM) performance targets; vi. prevents a liquidity risk from becoming a solvency risk.

2.3.2  Multi-Currency ALM System The main features and components of an ALM solution include multi-currency chart of accounts, net interest income simulation, static and dynamic gap analysis, non-maturity deposit model, dynamic portfolio assumptions, market-based valuations, term structure modeling, option-adjusted valuation, dynamic scenario analysis, earnings at risk, and risk-adjusted return on capital. The main modules are as follows: •

Extraction of data from the source systems

•

Static ALM •

Maturity mismatch

•

Cash flows

•

Economic value of equity (EVE) sensitivities*

•

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Stochastic ALM •

Earnings at risk*

•

Term structure models

•

Market rate model

•

Historic rate generator

Liquidity risk management* •

Stress scenarios*

•

Contingency plans*

*These topics are explained in Chapter 10 in the context of an enterprise liquidity hub. Figure 2-16 provides an overview of the components of the ALM. The system could have an FTP module or a bank might be using a standalone system for FTP.

Figure 2-16.  Asset liability management system

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The following are examples of product information uploaded into the ALM system: •

Retail loans – Mortgages form a separate group in the ALM system;

•

Retail collateral – Collateral type, mark-to-market value; retail deposits

•

Corporate loans; corporate collateral – Collateral type, mark-tomarket value; corporate deposits

•

Loans and deposits with embedded options are separate groups;

•

Funded trade finance

•

Treasury banking book; trading book

•

Cash and equivalents

•

Other assets (fixed assets)

•

Other borrowing

•

Other liabilities (capital/debt; accounts payable)

•

Off–balance sheet items – Guarantee, standby letters of credit, derivatives.

After the ALM system is populated, the following are the important tasks: •

Setting up the chart of accounts in the ALM system. It reflects the balance sheet and ALM modeling requirements.

•

Configuring the chart of accounts in the ALM system with specialized definitions that reflect specific balance sheet behavior;

•

Configuring the behavior of financial products for prepayments and other options.

The ALM system configuration is consistent with the needs of FTP, cost allocation, budgets, and regulatory reporting. Figure 2-17 provides insight into the data types required from the source systems for the ALM system.

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Figure 2-17.  Aggregated data for asset liability management system

Chart of Accounts & Aggregating Risk Positions Most ALM solutions provide an “ALM Chart of Account” for mapping the source system data. The charts of account support the definition of the parent–child structure and the definition of relationships for position aggregation. A bank could have more than one ALM chart of account to facilitate the analysis of different consolidated views; e.g., by geography, currency, asset, asset type, and term to maturity.

Cash-Flow Modeling, Monitoring, Forecasting “Bucket delta” refers to the sensitivity of cash time buckets to movements in the interest rate curve. Cash-flow aggregation and delta bucketing methods are applicable to banking products that have linear cash flows. The key objectives of cash-flow models are to (i) detect maturity mismatches and (ii) detect cash-flow disruptions caused by behavioral changes. The latter is explained under “Data Management and Advanced Analytical Applications” in Chapter 7, Sections 7.4.1.4, 7.5.3, and 7.5.5, and in Chapter 9, Section 9.3.10. The ALM system should support ALCO decision making by

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identifying liquidity gaps;

•

identifying structural liquidity issues in the balance sheet;

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•

assessing funding diversification; and

•

providing data on the risk inherent in the net interest margin; Section 2.3.5 of this chapter explains the NIM data dimensions, and riskadjusted NIM is explained in Chapter 10, Section 10.5.4.

2.3.3  ALM Risks There are two types of liquidity risks: (a) market liquidity risk is the inability to sell assets or obtain adequate funding on reasonable terms; and (b) funding liquidity risk is defined as the risk arising from a bank’s inability to meet its obligations as they become due without incurring unacceptable losses. The shape of the yield curve at a stated time depends on interest rate expectation, liquidity preference, and the behavior of borrowers and lenders. An ALM strategy is sensitive to any changes to the yield curve in the short and medium terms. Figure 2-18 illustrates the importance of the term structure in ALM. A floating-rate instrument carries an interest rate that is a base rate plus a spread. A variable-rate note carries an interest rate set by an internal process. The difference is that the floating-rate note depends only on the reference rate, which is usually a low-risk rate such as the LIBOR. The rate on a variable-rate note also considers the credit of the borrower as well as market factors.

Figure 2-18.  Interest rate term structure & ALM The repricing risk, yield curve risk, basis risk, option risk, and forward gap risk are explained in Chapter 1, Section 1.2.4. These risks have a direct impact on liquidity, earnings, and capital. 153

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In funding-liquidity-risk analysis, the behavior of counterparties under normal conditions and in stress scenarios is assessed. It is assessed on an ongoing basis or on a run-on-the-bank basis. This assessment includes •

all outflows that could occur at the earliest possible date;

•

drawdown of credit lines;

•

replication of non-maturing contracts;

•

prepayments; and

•

rollovers.

The following are common methods by which to measure liquidity risk: •

Using maturity mismatch between the contractual inflows and outflows for defined time buckets

•

Identifying over-reliance on funding sources

•

Checking the availability of unencumbered assets

Liquidity coverage and net stable funding ratios (LCR and NSFR) are mentioned in Section 2.3.4 and explained in Chapters 7 and 10 under “Enterprise Data Architecture” and “Enterprise Liquidity Management.”

Causal Events for Liquidity Risk Derivatives can introduce complex interest rate risk (IRR) exposures. Depending on the specific instrument, derivatives create repricing, basis, yield curve, option, or price risk. Embedded options associated with instruments on both sides of the balance sheet and off–balance sheet derivative exposure can create IRR.15 The following are causal events:

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In non-maturity deposits (NMDs), depositors could withdraw funds at any time.

•

The issuer of callable bonds can redeem the full amount or part of it before maturity.

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In wholesale borrowing, lenders could have a call option that requires the borrowing bank to repay, or a borrowing bank might have a put option that allows it to make repayments in advance against their borrowings.

•

In mortgage loans, borrowers can have an option to partially or fully pre-pay the loan.

All the above events alter the cash flows.

Event-driven architecture is explained in Chapter 7, Section 7.1.5.3.3, and Chapter 8, Section 8.2.16 explains how causal analytics are important aspects of intelligent, event-driven enterprise risk-adjusted return management.

I RR Management A bank’s IRR measurement quantifies earning-risk exposure and its impact on capital adequacy. The ALM system monitors and reports on the maturities of all assets and liabilities. It enables the bank to measure and control interest rate risk. The tools and techniques include the following: •

Static gap analysis refers to aggregating positions by using maturity and repricing dates. Table 2-26 provides an example of a gap analysis report. Deposits without a fixed maturity are included in the “less than one month” bucket in the table.

•

Dynamic gap analysis balances earnings and value.

•

Duration and convexity includes PV01, monetary and Macaulay duration, and convexity.

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Table 2-26.  Gap Analysis Based on Maturity and Repricing Dates

2.3.4  ALM Metrics Liquidity risks can be divided into structural and short-term liquidity risks. •

Structural liquidity risks are in the balance sheet and pose a challenge to LCR and NSFR management.

•

A bank is exposed to short-term liquidity risk when there is a likelihood that it might be unable to repay liabilities that mature in the near term. The LCR regulatory framework defines the most immediate time period as the next 30 days.

•

The objective of the net stable funding ratio (NSFR) is to guide a bank to maintain a stable funding profile over a one-year time horizon.

The core set of ALM tools are as follows: i. Ratio Analysis – Section 2.3.4.1 ii. Funding Matrix – Section 2.3.4.2

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iii. Gap Analysis – Section 2.3.4.3 iv. Duration – Section 2.3.4.4 v. Convexity – Section 2.3.4.5

2.3.4.1  Ratio Analysis The following ratios are an important aspect of static ALM: •

Liquidity risk measurement •

•

•

•

The cost of liquidity can be measured at desired frequencies by analyzing the yield curve and swap curve.

Cost impact ratio tracks the following: •

Quantum of high-cost funds to create assets: purchased funds/ total assets

•

Share of volatile liabilities in total deposits: volatile liabilities/ total deposits

•

Share of assets funded through volatile liabilities: volatile liabilities/total assets

Cash flow impact ratios track the following: •

Funding nature of assets core: customer deposits/total assets

•

Concentration of deposits: large deposits/total deposits

•

Concentration of advances: large advances/total advances

•

Total of “off–balance sheet” commitments to “on balance sheet.”

Loan deposit ratio •

It can vary according to banking supervisor’s guidelines. Excessive lending can expose a bank to liquidity and interest rate risk.

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Medium-term funding ratio •

Borrowing on a short-term basis and lending it for a longer term leads to maturity mismatch. Medium-term funding ratio is the ratio of liabilities with a residual contractual maturity of more than one year, to assets with a residual contractual maturity of more than one year. This reflects a bank’s ability to roll over shortterm deposits in order to fund medium-term assets.

2.3.4.2  Funding Matrix The time bucket–based funding matrix is a fundamental tool for maintaining structural medium- and long-term liquidity. It is based on planned cash flows by currency. All funding-relevant assets and liabilities are mapped into time buckets corresponding to their contractual maturities. The bank identifies excesses and shortfalls for each time bucket.

Funding matrix explained in Chapter 10, Section 10.1.6.2, in the context of enterprise liquidity management and the enterprise liquidity hub. Dynamic ALM is explained in Section 10.2.

2.3.4.3  Rate-Sensitivity Gap Analysis The funding analysis includes a report that categorizes assets and liabilities using repricing periods. This improves the monitoring of the sensitivity of the balance sheet to rate changes. The core components of gap management are the repricing model and the management of the net interest margin (NIM). Rate-sensitive assets (RSA) or ratesensitive liabilities (RSL) must mature or be repriced. The following are the dimensions and data items of the gap analysis: The rate-sensitivity gap (RSG) is a ratio of rate-sensitive assets (RSA) to rate-sensitive liabilities (RSL). RSG = RSA/RSL I = interest rate; Δi = change in interest rate NW = Net Worth

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Gapi = (RSAi - RSLi) for maturity bucket or period I; e.g., 30, 31–90, 91–180, 181–365, >365 days Relative Gap = (RSA – RSL)/total assets Cumulative Gapi = sum of all gaps up to maturity i ΔNet Interest Incomei = Gapi X (Δi) A = (RSA + NRSA) and L = (RSL + NRSL) NII = (A x iA) – (L x iL) ΔNII = (RSA x ΔiRSA) – (RSL x ΔiRSL) + [(NRSA x 0) – (NRSL x 0)] Gap assumes ΔiRSA = ΔiRSL = Δi and no change in volume or mix of RSA & RSL

Table 2-27.  Gap – Rate-Sensitive Assets, Rate-Sensitive Liabilities Gap

Δi ΔNII

Comment

Positive

+ +

positioned for increase in rates, or asset sensitive

Positive

-

positioned for increase in rates, or asset sensitive

Negative

+ -

positioned for decrease in rates, or liability sensitive

Negative

-

positioned for decrease in rates or, liability sensitive

Zero

+ 0

+

Implications A positive rate sensitivity gap (RSG) implies that there are more interest rate–sensitive assets (in paramount) than interest rate–sensitive liabilities. As interest rates rise, the return on assets will rise faster than funding costs, resulting in a higher spread income. If RSG is negative, funding costs will rise at a faster rate than will the return on assets.

2.3.4.4  Duration Gap (DGAP) Analysis Banks evaluate the effects of changing interest rates on a bank’s economic value by applying sensitivity weights to each time bucket. The weights are based on estimates of the duration of the assets and liabilities. 159

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Duration Gap Model An application of the duration gap model consists of the following steps: i. Forecast the interest rates. ii. Calculate the market value for all balance sheet items. iii. Calculate the duration of each balance sheet item. iv. Calculate the duration gap. v. Immunize the bank balance sheet by reducing DGAP to zero. A bank can manage part or all of its interest rate risk by matching assets to liabilities using tools such as (a) matching the average duration of assets and liabilities and (b) using derivatives to create an immunization overlay (hedge). Table 2-28 provides a template for calculating total asset duration and total liability duration.

Table 2-28.  Total Asset Duration, Total Liability Duration

Duration analysis enables banks to assess the impact of interest rate changes on the balance sheet. Duration Gap = DA – (DL x (L/A)) Banks with longer-term assets funded by shorter-term liabilities will generally have a duration gap that is positive. The greater the duration gap, the more exposed the bank’s Economic Value of Equity (EVE) is to rising interest rates.

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Table 2-29a.  Duration Gap Duration gap sign effects (> or or < Change in Liability Change in NW

Duration Gap

+

+

-

>

-

-

+

-

+

>

+

+

-

+

-