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Prelims

Angelo Corelli (Maastricht School of Management, The Netherlands)

Understanding Financial Risk Management, Third Edition

ISBN: 978-1-83753-253-7, eISBN: 978-1-83753-250-6

Publication date: 27 May 2024

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Citation

Corelli, A. (2024), "Prelims", Understanding Financial Risk Management, Third Edition, Emerald Publishing Limited, Leeds, pp. i-xxv. https://doi.org/10.1108/978-1-83753-250-620243023

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Emerald Publishing Limited

Copyright © 2024 Angelo Corelli

Half Title Page

Understanding Financial Risk Management

Third Edition

Title Page

Understanding Financial Risk Management

Third Edition

By

Angelo Corelli

Maastricht School of Management, The Netherlands

United Kingdom – North America – Japan – India – Malaysia – China

Copyright Page

Emerald Publishing Limited

Emerald Publishing, Floor 5, Northspring, 21-23 Wellington Street, Leeds LS1 4DL.

Third edition 2024

Copyright © 2024 Angelo Corelli. Published under exclusive license by Emerald Publishing Limited.

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No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use.

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ISBN: 978-1-83753-253-7 (Print)

ISBN: 978-1-83753-250-6 (Online)

ISBN: 978-1-83753-252-0 (Epub)

Dedication

“Education breeds confidence. Confidence breeds hope. Hope breeds peace.”

Confucius

Contents

List of Tables xv
List of Figures xix
Preface to the Second Edition xxiii
Addendum to the Preface xxv
Chapter 1: Risk: An Overview 1
1.1. Introduction 2
1.1.1. Randomness and Uncertainty 2
1.1.2. Rationality and Risk Aversion 5
1.1.3. Types of Risk 10
Snapshot 1.1: Common Forms of Utility Functions 14
1.2. The Process of Risk Management 15
1.2.1. Risk in Corporations and Financial Institutions 15
1.2.2. Identification, Measurement and Mitigation 19
1.2.3. Risk Response Strategies 21
1.3. Theory of Markets 22
1.3.1. Arbitrage 23
1.3.2. The EMH 25
1.3.3. Brownian Motion 28
Snapshot 1.2: Sampling of Brownian Motion Paths in Excel 32
Summary 33
Bibliography 34
Exercises 34
Appendix: Types of Market Failure 37
Chapter 2: Financial Markets and Volatility 39
2.1. Modern Portfolio Theory 40
2.1.1. The Risk/Return Trade Off 40
2.1.2. Optimal Portfolios of Risky Assets 45
2.1.3. Optimal Portfolios with Risk-free Asset 48
Snapshot 2.1: Portfolio Optimization in Excel 51
2.2. The CAPM 52
2.2.1. Model Assumptions 52
2.2.2. The SML 55
2.2.3. Beyond CAPM 60
2.3. Volatility and Correlation 63
2.3.1. Types of Volatility 63
2.3.2. Correlation Versus Covariance 66
2.3.3. Maximum Likelihood Methods 69
Snapshot 2.2: The Covariance Matrix of Financial Returns 72
Summary 73
Bibliography 74
Exercises 74
Appendix: The Table of the Standard Normal Distribution 78
Chapter 3: Conditional Dependence and Time Series 81
3.1. Modeling Financial Comovements 82
3.1.1. Conditional Covariance 82
3.1.2. Conditional Correlation 83
3.2. Time Series Analysis 85
3.2.1. ARCH/GARCH Models 85
3.2.2. Autocorrelation of Financial Returns 89
3.2.3. Other Stylized Facts 93
Summary 95
Bibliography 96
Chapter 4: Statistical Analysis 97
4.1. Relevant Distributions 98
4.1.1. Pareto Distribution 98
4.1.2. Binomial Distribution 102
4.1.3. Poisson Distribution 106
Snapshot 4.1: Excel Statistical Functions 111
4.2. Probabilistic Approaches 112
4.2.1. Scenario Analysis 112
4.2.2. Decision Trees 113
4.2.3. Simulations 116
Summary 118
Bibliography 119
Exercises 120
Appendix: Ito’s Lemma 123
Chapter 5: Financial Derivatives 127
5.1. Options and Futures 128
5.1.1. Types of Traders in the Market 128
5.1.2. Option Structure and Payout 131
5.1.3. Forward and Futures 134
Snapshot 5.1: Volatility Strategy with Strangles 138
5.2. Interest Rate Derivatives 139
5.2.1. Interest Rate Swaps 139
5.2.2. Caps and Floors 141
5.2.3. Swaptions 144
Summary 147
Bibliography 147
Exercises 148
Appendix: The Market Price of Risk 150
Chapter 6: Option Pricing and Risk Modeling 153
6.1. Option Pricing Models 154
6.1.1. Binomial Trees 154
6.1.2. BSM Model 158
6.2. Portfolio Hedging 163
6.2.1. Delta Hedging 163
6.2.2. Gamma and Vega Hedging 165
6.2.3. The Cost of Hedging 167
Summary 170
Bibliography 170
Exercises 170
Chapter 7: Market Risk 173
7.1. Market Risk Metrics 174
7.1.1. Overview of Market Risk 174
7.1.2. Quantile Metrics and VaR 176
7.1.3. VaR Rationale and Definition 180
Snapshot 7.1: The Choice of Parameters for VaR 183
7.2. VaR Calculation Methods 184
7.2.1. Historical Simulation Approach 184
7.2.2. Parametric Method 185
7.2.3. Monte Carlo Simulation 187
Snapshot 7.2: Euler’s Theorem on Homogeneous Functions 189
Summary 190
Bibliography 191
Exercises 192
Appendix: Factor Mapping for VaR 193
Chapter 8: Inside Value at Risk 195
8.1. VaR Features 195
8.1.1. Decomposition 196
8.1.2. Limitations 199
8.1.3. Analytic Approximations 201
8.2. VaR Testing 203
8.2.1. Model Backtesting 203
8.2.2. Stress Testing 206
Summary 207
Bibliography 208
Chapter 9: Interest Rate Risk 209
9.1. The Dynamics of Interest Rates 210
9.1.1. Bond Prices and Yields 210
9.1.2. Fixed Income Futures 215
9.1.3. Yield Shifts and Immunization 218
Snapshot 9.1: Compounding Frequencies for Interest Rates 224
9.2. Short Rate Models 224
9.2.1. The Term Structure of Interest Rates 224
9.2.2. Single-factor Models 228
9.2.3. Multi-factor Models 232
9.3. IRR Management 234
9.3.1. Sources and Identification 234
9.3.2. Measurement Techniques 236
9.3.3. Duration and Convexity Hedging 238
Summary 242
Bibliography 243
Exercises 243
Appendix: Principal Component Analysis of the Term Structure 246
Chapter 10: Credit Risk 249
10.1. Basic Concepts 250
10.1.1. Default Probabilities 250
10.1.2. Loss Given Default 253
10.1.3. Credit Ratings 257
10.2. Structural Models 260
10.2.1. The KMV-Merton Approach 260
10.2.2. First Passage Models 264
10.2.3. CreditMetrics™ 267
10.3. Reduced-form Models 269
10.3.1. Jarrow–Turnbull Model 269
10.3.2. The Duffie–Singleton Model 271
10.3.3. CreditRisk+™ 273
Summary 276
Bibliography 276
Exercises 277
Appendix: Markov Process for Transition Matrices 280
Chapter 11: Commodity Risk 283
11.1. Commodity Markets 283
11.1.1. Commodity Types and Classification 284
11.1.2. The Risk for Traders and Investors 286
11.2. Commodity Risk Hedging 288
11.2.1. Commodity Futures and Forward Contracts 288
11.2.2. Commodity Options and Swaps 292
Summary 296
Exercises 296
Bibliography 297
Chapter 12: Liquidity Risk 299
12.1. Market Prices 300
12.1.1. Market Microstructure 300
12.1.2. Price Formation 304
12.1.3. Funding Versus Market Liquidity 306
Snapshot 12.1: Liquidity Black Holes 311
12.2. Liquidity Models 312
12.1.1. Theoretical Models 312
12.2.2. Traceable Models 316
12.2.3. The Diamond–Dybvig Model 320
12.3. Liquidity Risk and Regulation 323
12.3.1. Liquidity Coverage Ratio 323
12.3.2. Net Stable Funding Ratio 326
12.3.3. Monitoring Tools 328
Summary 330
Bibliography 331
Exercises 332
Appendix: Liquidity Capital Asset Pricing Model 335
Chapter 13: Enterprise Risk 337
13.1. The Fundamentals 338
13.1.1. Identification and Assessment 338
13.1.2. The ERM Framework 341
13.1.3. The COSO ERM 342
13.2. Building and Enhancing Capabilities 345
13.2.1. Improving the Process View 345
13.2.2. Technological Capabilities 348
13.3. Practical Implementation 350
13.3.1. The Role of the Management 350
13.3.2. Implementation and Models 352
Summary 353
Bibliography 354
Chapter 14: Other Risks 355
14.1. Operational Risk 356
14.1.1. Identification and Assessment 356
14.1.2. Treatment and Control 359
14.1.3. Basel II Approach 360
14.2. Currency Risk 365
14.2.1. Types of Currency Risk 365
14.2.2. Foreign Exchange Derivatives 367
14.2.3. Risk Hedging in FX Markets 371
14.3. Volatility Risk 373
14.3.1. Implied Volatility 373
14.3.2. Callable Bonds 375
14.3.3. Variance Swaps 378
Snapshot 14.1: Gamma Swaps 381
Summary 382
Bibliography 382
Exercises 383
Appendix: Risk-Adjusted Return on Capital 384
Chapter 15: Beyond Normality and Correlation 387
15.1. Copula Functions 388
15.1.1. Basic Properties 388
15.1.2. Measures of Dependence 391
15.1.3. Application to Risk Management 394
Snapshot 15.1: Monte Carlo Simulation of Copulas 397
15.2. Extreme Value Theory 397
15.2.1. Theoretical Background 397
15.2.2. Data Application 401
15.2.3. Extreme VaR 402
15.3. Beyond VaR 404
15.3.1. Model Backtesting 404
15.3.2. Expected Shortfall 407
15.3.3. Conditional VaR 409
Summary 411
Bibliography 412
Exercises 413
Appendix: VaR for Portfolios of Derivatives 415
Chapter 16: Conditional Risk Analysis 417
16.1. Beyond VaR 418
16.1.1. Expected Shortfall 418
16.1.2. Conditional VaR 420
16.2. Multivariate Return Distributions 422
16.2.1. GARCH (p,q) Modeling 422
Summary 424
Bibliography 425
Chapter 17: High-frequency Data 427
17.1. High-frequency Trading 427
17.1.1. Data Filtering 427
17.1.2. Basic Stylized Facts 430
17.2. Intraday Risk Analysis 431
17.2.1. Heterogeneous Volatility 431
Summary 434
Bibliography 434
Exercises 435
Appendix: Power Laws for Intraday Data 436
Chapter 18: Financial Crisis and Securitization 439
18.1. Crisis and Regulation 440
18.1.1. The Lack of Regulatory Framework 440
18.1.2. The Crisis in Europe 444
18.1.3. The Impact on the Financial Industry 448
18.2. Credit Derivatives 450
18.2.1. Asset Swaps 450
18.2.2. Credit Default Swaps 454
18.2.3. CDS Spreads with Counterparty Credit Risk 458
Snapshot 18.1: The Newton–Raphson Method 460
18.3. Securitization 461
18.3.1. Structure and Participants 461
18.3.2. Collateralized Debt Obligations 463
18.3.3. Advantages and Disadvantages 467
Summary 469
Bibliography 470
Exercises 471
Appendix: A Model of SPVs 472
Chapter 19: Hedging Techniques 475
19.1. Market Risk Hedging 476
19.1.1. Delta Hedging 476
19.1.2. Gamma and Vega Hedging 478
19.1.3. The Cost of Hedging 480
19.2. Credit Risk Hedging 483
19.2.1. Modeling Exposure 483
19.2.2. Credit Value Adjustment 487
19.2.3. Monte Carlo Methods 491
19.3. Advanced IRR Hedging 494
19.3.1. M-Absolute and M-squared Models 494
19.3.2. Duration Vectors 496
19.3.3. Hedging with Fixed Income Derivatives 499
Snapshot 19.1: Convexity Adjustment for Interest Rate Derivatives 501
Summary 502
Bibliography 503
Exercises 504
Chapter 20: Advanced Topics 507
20.1. VaR Advances 507
20.1.1. Modified Delta-VaR 508
20.1.2. Historical Simulation Revisited 511
20.1.3. Modified MC and Scenario Analysis 513
20.2. Alternative Risk Transfer 514
20.2.1. The ART Market 514
20.2.2. Primary Contracts 516
20.2.3. Insurance Derivatives 519
Summary 521
Bibliography 522
Exercises 523
Chapter 21: Digital Finance and Risk 525
21.1. The Fintech Revolution 526
21.1.1. Introduction 526
21.1.2. The Role of Big Data 529
21.1.3. Fintech and Risk Management 532
21.2. Derivatives on Bitcoin 536
21.2.1. Hedging Techniques 536
21.2.2. The Impact on Markets and Investments 538
Summary 540
Bibliography 540
Chapter 22: The Future of Financial Risk Management 543
22.1. The Role of Corporate Governance 544
22.1.1. Management Fails 544
22.1.2. Remuneration and Incentive Systems 547
22.1.3. Postcrisis Perspectives 549
22.2. The Banking Sector 550
22.2.1. Bank Risk and Business Models 550
22.2.2. Risk Management Systems 552
22.2.3. Areas of Future Improvements 556
22.3. Challenges for Research 558
22.3.1. Interbank Risk 558
22.3.2. Energy Derivatives 559
22.3.3. Sovereign Risk Dynamics 562
Summary 565
Bibliography 566
Exercises 567
Index 569

List of Tables

Table 1.1 Risk Likelihood 20
Table 1.2 Risk Impact 20
Table 1.3 Risk Priority 21
EXTable 2.1 51
EXTable 2.2 51
EXTable 2.3 51
EXTable 2.4 69
EXTable 2.5 69
EXTable 2.6 75
EXTable 2.7 76
EXTable 2.8 76
EXTable 2.9 76
EXTable 2.10 77
EXTable 2.11 78
EXTable 2.12 79
Table 3.1 Constructing a Series of Lagged Values Implies Shifting Down the Observations in Order to Match the Previous Date. Then It Is Possible to Get the Difference Between the Old Value and the Lagged One (First Difference). 90
EXTable 4.1 111
EXTable 4.2 111
EXTable 4.3 112
EXTable 4.4 113
EXTable 4.5 121
EXTable 4.6 122
Table 5.1 No-arbitrage Argument for Spot-forward Payoff Equality 135
EXTable 5.1 148
Table 9.1 Effective Annual Rate Calculation for Different Compounding Frequencies 211
Table 9.2 Compounding Frequencies 211
EXTable 9.1 215
EXTable 9.2 217
EXTable 9.3 218
EXTable 9.4 244
EXTable 9.5 244
EXTable 9.6 245
EXTable 9.7 245
Table 10.1 Credit CF for PFE Calculation 254
EXTable 10.1 255
Table 10.2 Credit Ratings Assigned by the Major Credit Agencies 258
Table 10.3 Credit Ratings on Sovereign Countries 259
Table 10.4 Altman’s z Score Factors and Weights 259
EXTable 10.2 260
Table 10.5 A Typical Example of a Credit Ratings Transition Matrix. For Each Transition from a Rating to Another, the Corresponding Transition Probability Is Shown 268
EXTable 10.3 277
EXTable 10.4 278
EXTable 10.5 279
Table 11.1 Classification of Commodities with Examples 284
Table 11.2 Specifications of a Gold Futures Contract Traded on CME Globex 287
Table 11.3 Replication of a Forward Contract by Using the Underlying Asset 290
Table 12.1 Run-off Rates for the Major Asset Categories 326
Table 12.2 RSF Factors for the Major Category Components 328
EXTable 12.1 333
EXTable 12.2 333
EXTable 12.3 333
Table 14.1 Operational Income Factors and Indicators for the Different Business Lines in the Bank 363
Table 19.1 Volatility Spread Approximations 487
Table 19.2 Add-On Percentages of the Underlying Amount for Different Types of Contract 489
EXTable 19.1 504
EXTable 19.2 505
Table 21.1 Summary of Fintech Applications for Financial Purposes 526
Table 21.2 Summary of the Main Specifications of the CME Bitcoin Futures Markets, Common to All Cryptocurrencies with Contracts Settled in USD 536
Table 21.3 Summary of the Main Specifications of the ETN Bitcoin Futures Markets 538

List of Figures

Fig. 1.1 Graph Concave Utility Function. 9
Fig. 1.2 Diversification. 11
Fig. 1.3 Risk Process. 16
Fig. 1.4 Information Subsets. 27
Fig. 2.1 Normal Distribution 1. 41
Fig. 2.2 Normal Distribution 2. 42
Fig. 2.3 Normal Distribution 3. 42
Fig. 2.4 Efficient Frontier 1. 46
Fig. 2.5 CML. 50
Fig. 2.6 Leverage. 50
Fig. 2.7 SML. 58
Fig. 2.8 SML Alphas. 59
Fig. 3.1 Autocorrelation. 90
Fig. 3.2 Auto Correlation Function. 91
Fig. 4.1 Pareto_1. 100
Fig. 4.2 Binomial. 105
Fig. 4.3 Poisson. 108
Fig. 4.4 Tree Nodes. 114
Fig. 4.5 Tree Example. 115
Fig. 4.6 Tree Example 2. 116
Fig. 5.1 Long Call. 132
Fig. 5.2 Short Call. 132
Fig. 5.3 Long Put. 133
Fig. 5.4 Short Put. 133
Fig. 5.5 Forward. 135
Fig. 5.6 Strangles. 138
Fig. 6.1 Binomial Tree. 154
Fig. 6.2 Price Tree. 157
Fig. 7.1 The figure shows the corresponding VaR at the 99% level of confidence. 177
Fig. 7.2 VaR can be calculated for several confidence intervals. 179
Fig. 9.1 Yield_Shift_1. 219
Fig. 9.2 Yield_Shift_2. 220
Fig. 9.3 Yield_Shift_3. 220
Fig. 9.4 Yield Curve. 225
Fig. 10.1 KMV. 263
Fig. 10.2 CMetrics. 267
Fig. 10.3 Thresholds. 268
Fig. 11.1 Forward. 289
Fig. 11.2 Collar. 294
Fig. 11.3 BrSpr. 294
Fig. 11.4 CmSwp. 295
Fig. 12.1 Liquidity. 308
Fig. 14.1 Structure of Internal Controls. 361
Fig. 14.2 Loss Frequency and Severity. 362
Fig. 14.3 Duration Callable. 377
Fig. 14.4 Convexity. 378
Fig. 15.1 A Stylized Scheme of a Gaussian Copula (Left) and a Student-t Copula (Right). 390
Fig. 15.2 A Stylized Scheme of a Clayton Copula (Left) and a Frank Copula (Right). 391
Fig. 15.3 A Stylized Scheme of a Gumbel Copula. 391
Fig. 15.4 The Schematic Representation of the Frechét Distribution (Left) and the Weibull Distribution (Right). 400
Fig. 15.5 The Schematic Representation of the Gumbel Distribution. 400
Fig. 18.1 Asset Swap. 451
Fig. 18.2 Market Asset Swap. 452
Fig. 18.3 CDS. 455
Fig. 18.4 Securitization. 462
Fig. 18.5 Tranches. 464
Fig. 18.6 CDO. 465
Fig. 18.7 ABS CDO. 465
Fig. 20.1 Captives. 516
Fig. 20.2 Multirisk. 518
Fig. 20.3 Cat Swap Before. 520
Fig. 20.4 Cat Swap After. 521
Fig. 20.5 Schematic Representation of the Seasonality (Left) and Volatility Clustering (Right) in High-frequency Financial Time Series. 522
Fig. 21.1 Blockchain. 527
Fig. 21.2 Big Data. 530
Fig. 21.3 Crime Detection. 532
Fig. 21.4 Cryptorisk. 534
Fig. 21.5 Cryptostorage. 535
Fig. 21.6 Rolling Yield. 537
Fig. 22.1 Board. 544
Fig. 22.2 Remuneration. 547
Fig. 22.3 Banking. 552
Fig. 22.4 Oil Swap. 561

Preface to the Second Edition

The second edition of Understanding Financial Risk Management aims to improve the first edition by introducing a more structured approach to the sources of risk in the organization, and the methods used to manage it.

From identification to assessment and management, all types of financial risks a company faces daily are analyzed, together with the tools and techniques that can be used to limit their impact and manage their connected risk events.

Built on the solid pedagogical approach used in the first edition, the second edition improves it by extending the narrative to modern and innovative topics like enterprise risk.

The result is a 22-chapter textbook that takes the student into a full-immersion experience. After an introductory part where distributional issues, statistical tools and other foundation topics are analyzed, the chapters start digging deep into all types of financial risk that are normally presented to the organization on a daily basis.

An improved coverage of major risks, together with ample narrative on how to use financial derivatives to hedge risk, offer a complete view on past, current and future trends in financial risk management.

Addendum to the Preface

The third edition of Understanding Financial Risk Management features a separated chapter for high-frequency data, as well as two new chapters, on commodity risk and digital finance and risk, respectively.

A general refinement has been done throughout the text in terms of editing and corrections, in order to improve the learning experience of the students and enhance the understanding of the various topics.

Overall, the book aims to keep a solid foundation of theory and applications, while looking forward to include modern topics and introduce the students to the digital era and other innovations.

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