Understanding Market, Credit, and Operational Risk The Value at Risk Approach

by Allen, Linda; Boudoukh, Jacob; Saunders, Anthony

• ISBN13: 9780631227090
• ISBN10: 0631227091
• Edition: 1st
• Format: Hardcover
• Copyright: 2003-12-30
• Publisher: Wiley-Blackwell

Summary

A step-by-step, real-world guide to the use of Value at Risk (VaR) models, this text applies the VaR approach to the measurement of market risk, credit risk, and operational risk. The book describes and critiques proprietary models, illustrating them with practical examples drawn from actual case studies. Explaining the logic behind the economics and statistics, this technically sophisticated yet intuitive text should be an essential resource for all readers operating in a world of risk. The text uses VaR techniques to analyze loans, derivatives, equity prices, foreign currencies and other financial instruments. Featuring comprehensive coverage of the BIS bank capital requirements, and including the latest proposals for the New Capital Accord, the book also describes the newest application of VaR techniques to operational risk measurement. The text examines the promise and the pitfalls of these risk measurement models, and makes recommendations for future research into this important area.

Author Biography

Linda Allen is Professor of Finance at the Zicklin School of Business at Baruch College, City University of New York, and Adjunct Professor of Finance at the Stern School of Business, New York University. She is also the author of Capital Markets and Institutions: A Global View and co-author of Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms, (2nd edition). She is an associate editor of the Journal of Banking and Finance, Journal of Economics and Business, and Multinational Finance Journal, and has published extensively in top academic journals in finance and economics.

Jacob Boudoukh is Professor of Finance and the founding director of theCaesarea Edmond Benjamin de Rothschild Center for Capital Markets and Risk Management at the Arison School of Business, IDC; as well as holding positions at the Stern School of Business, New York University. ormerly formerly with and currently visiting Stern-NYU; and a member of the NBER. His work has been published in academic journals such as The American Economic Review, and The Journal of Financial Economics, as well as practitioner journals such as Risk.Anthony Saunders is John M. Schiff Professor of Finance and Chair of the Department of Finance at the Stern School of Business, and Economics and Finance Department Chair at New York University. He is also editor of the Journal of Banking and Finance and the Journal of Financial Markets, Institutions and Instruments, and has published Financial Institutions and Management (2nd4th edition). Professor Saunders has published widely in top journals such as Journal of Finance.

Table of Contents

List of Figures	xiv
List of Tables	xvi
Preface	xviii
List of Abbreviations	xx
1 Introduction to Value at Risk (VaR)	1	(20)
1.1 Economics underlying VaR measurement	2	(11)
1.1.1 What is VaR?	4	(2)
1.1.2 Calculating VaR	6	(2)
1.1.3 The assumptions behind VaR calculations	8	(2)
1.1.4 Inputs into VaR calculations	10	(3)
1.2 Diversification and VaR	13	(8)
1.2.1 Factors affecting portfolio diversification	16	(1)
1.2.2 Decomposing volatility into systematic and idiosyncratic risk	17	(1)
1.2.3 Diversification: Words of caution - the case of long-term capital management (LTCM)	18	(3)
2 Quantifying Volatility in VaR Models	21	(61)
2.1 The stochastic Behavior of Returns	22	(13)
2.1.1 Revisiting the assumptions	22	(1)
2.1.2 The distribution of interest rate changes	23	(2)
2.1.3 Fat tails	25	(1)
2.1.4 Explaining fat tails	26	(3)
2.1.5 Effects of volatility changes	29	(2)
2.1.6 Can (conditional) normality be salvaged?	31	(3)
2.1.7 Normality cannot be salvaged	34	(1)
2.2 VaR Estimation Approaches	35	(24)
2.2.1 Cyclical volatility	36	(1)
2.2.2 Historical standard deviation	36	(2)
2.2.3 Implementation considerations	38	(2)
2.2.4 Exponential smoothing - RiskMetrics™ volatility	40	(8)
2.2.4.1 The optimal smoother lambda	43	(1)
2.2.4.2 Adaptive volatility estimation	44	(1)
2.2.4.3 The empirical performance of RiskMetrics™	45	(1)
2.2.4.4 GARCH	45	(3)
2.2.5 Nonparametric volatility forecasting	48	(6)
2.2.5.1 Historical simulation	48	(3)
2.2.5.2 Multivariate density estimation	51	(3)
2.2.6 A comparison of methods	54	(2)
2.2.7 The hybrid approach	56	(3)
2.3 Return Aggregation and VaR	59	(3)
2.4 Implied Volatility as a Predictor of Future Volatility	62	(4)
2.5 Long Horizon Volatility and VaR	66	(3)
2.6 Mean Reversion and Long Horizon Volatility	69	(2)
2.7 Correlation Measurement	71	(3)
2.8 summary	74	(1)
Appendix 2.1 Backtesting Methodology and Results	74	(8)
3 Putting VaR to Work	82	(37)
3.1 The VaR of Derivatives - Preliminaries	82	(15)
3.1.1 Linear derivatives	83	(3)
3.1.2 Nonlinear derivatives	86	(1)
3.1.3 Approximating the VaR of derivatives	86	(7)
3.1.4 Fixed income securities with embedded optionality	93	(2)
3.1.5 "Delta normal" vs. full-revaluation	95	(2)
3.2 structured Monte Carlo, Stress Testing, and scenario Analysis	97	(13)
3.2.1 Motivation	97	(1)
3.2.2 structured Monte Carlo	98	(3)
3.2.3 Scenario analysis	101	(9)
3.2.3.1 Correlation breakdown	101	(2)
3.2.3.2 Generating reasonable stress	103	(1)
3.2.3.3 Stress testing in practice	104	(2)
3.2.3.4 Stress testing and historical simulation	106	(1)
3.2.3.5 Asset concentration	107	(3)
3.3 Worst Case Scenario (WCS)	110	(3)
3.3.1 WCS vs. VaR	110	(1)
3.3.2 A comparison of VaR to WCS	111	(1)
3.3.3 Extensions	112	(1)
3.4 Summary	113	(1)
Appendix 3.1 Duration	114	(5)
4 Extending the VaR Approach to Non-tradable Loans	119	(39)
4.1 Traditional Approaches to Credit Risk Measurement	120	(8)
4.1.1 Expert systems	121	(1)
4.1.2 Rating systems	122	(2)
4.1.3 Credit scoring models	124	(4)
4.2 Theoretical Underpinnings: Two Approaches	128	(10)
4.2.1 Options-theoretic structural models of credit risk measurement	128	(4)
4.2.2 Reduced form or intensity-based models of credit risk measurement	132	(6)
4.2.3 Proprietary VaR models of credit risk measurement	138	(1)
4.3 CreditMetrics	138	(13)
4.3.1 The distribution of an individual loan's value	138	(5)
4.3.2 The value distribution for a portfolio of loans	143	(18)
4.3.2.1 Calculating the correlation between equity returns and industry indices for each borrower in the loan portfolio	144	(1)
4.3.2.2 Calculating the correlation between borrower equity returns	144	(1)
4.3.2.3 Solving for joint migration probabilities	145	(2)
4.3.2.4 Valuing each loan across the entire credit migration spectrum	147	(2)
4.3.2.5 Calculating the mean and standard deviation of the normal portfolio value distribution	149	(2)
4.4 Algorithmics' Mark-to-Future	151	(2)
4.5 Summary	153	(2)
Appendix 4.1 CreditMetrics: Calculating Credit VaR Using the Actual Distribution	155	(3)
5 Extending the VaR Approach to Operational Risks	158	(42)
5.1 Top-Down Approaches to Operational Risk Measurement	161	(9)
5.1.1 Top-down vs. bottom-up models	162	(1)
5.1.2 Data requirements	163	(2)
5.1.3 Top-down models	165	(5)
5.1.3.1 Multi-factor models	165	(1)
5.1.3.2 Income-based models	166	(1)
5.1.3.3 Expense-based models	167	(1)
5.1.3.4 Operating leverage models	167	(1)
5.1.3.5 Scenario analysis	167	(1)
5.1.3.6 Risk profiling models	168	(2)
5.2 Bottom-Up Approaches to Operational Risk Measurement	170	(15)
5.2.1 Process approaches	170	(6)
5.2.1.1 Causal networks or scorecards	170	(3)
5.2.1.2 Connectivity models	173	(2)
5.2.1.3 Reliability models	175	(1)
5.2.2 Actuarial approaches	176	(6)
5.2.2.1 Empirical loss distributions	176	(1)
5.2.2.2 Parametric loss distributions	176	(3)
5.2.2.3 Extreme value theory	179	(3)
5.2.3 Proprietary operational risk models	182	(3)
5.3 Hedging Operational Risk	185	(11)
5.3.1 Insurance	186	(2)
5.3.2 Self-insurance	188	(2)
5.3.3 Hedging using derivatives	190	(5)
5.3.3.1 Catastrophe options	191	(2)
5.3.3.2 Cat bonds	193	(2)
5.3.4 Limitations to operational risk hedging	195	(1)
5.4 Summary	196	(1)
Appendix 5.1 Copula Functions	196	(4)
6 Applying VaR to Regulatory Models	200	(33)
6.1 BIS Regulatory Models of Market Risk	203	(3)
6.1.1 The standardized framework for market risk	203	(2)
6.1.1.1 Measuring interest rate risk	203	(1)
6.1.1.2 Measuring foreign exchange rate risk	204	(1)
6.1.1.3 Measuring equity price risk	205	(1)
6.1.2 Internal models of market risk	205	(1)
6.2 BIS Regulatory Models of Credit Risk	206	(15)
6.2.1 The Standardized Model for credit risk	207	(2)
6.2.2 The Internal Ratings-Based Models for credit risk	209	(6)
6.2.2.1 The Foundation IRB Approach	210	(4)
6.2.2.2 The Advanced IRB Approach	214	(1)
6.2.3 BIS regulatory models of off-balance sheet credit risk	215	(3)
6.2.4 Assessment of the BIS regulatory models of credit risk	218	(3)
6.3 BIS Regulatory Models of Operational Risk	221	(10)
6.3.1 The Basic Indicator Approach	223	(1)
6.3.2 The Standardized Approach	224	(1)
6.3.3 The Advanced Measurement Approach	225	(6)
6.3.3.1 The internal measurement approach	227	(3)
6.3.3.2 The loss distribution approach	230	(1)
6.3.3.3 The scorecard approach	230	(1)
6.4 Summary	231	(2)
7 VaR: Outstanding Research	233	(3)
7.1 Data Availability	233	(1)
7.2 Model Integration	234	(1)
7.3 Dynamic Modeling	235	(1)
Notes	236	(21)
References	257	(13)
Index	270