The VAR Implementation Handbook

by Gregoriou, Greg

• ISBN13: 9780071615136
• ISBN10: 007161513X
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-03-12
• Publisher: McGraw-Hill Education

Summary

For investors, risk is about the odds of losing money, and Value at Risk (VaR) is grounded in that common-sense fact. VAR modeling answers, "What is my worst-case scenario?" and "How much could I lose in a really bad month?" However, there has not been an effective guidebook available to help investors and financial managers make their own VaR calculations--until now. The VaR Implementation Handbook is a hands-on road map for professionals who have a solid background in VaR but need the critical strategies, models, and insights to apply their knowledge in the real world. Heralded as "the new science of risk management," VaR has emerged as the dominant methodology used by financial institutions and corporate treasuries worldwide for estimating precisely how much money is at risk each day in the financial markets. The VaR Implementation Handbook picks up where other books on the subject leave off and demonstrates how, with proper implementation, VaR can be a valuable tool for assessing risk in a variety of areas-from equity to structured and operational products. This complete guide thoroughly covers the three major areas of VaR implementation--measuring, modeling risk, and managing--in three convenient sections. Savvy professionals will keep this handbook at their fingertips for its: Reliable advice from 40 recognized experts working in universities and financial institutions around the world. Effective methods and measures to ensure that implemented VaR models maintain optimal performance. Up-to-date coverage on newly exposed areas of volatility, including derivatives. Real-world prosperity requires making informed financial decisions. The VaR Implementation Handbook is a step-by-step playbook to getting the most out of VaR modeling so you can successfully manage financial risk.

Author Biography

Greg N. Gregoriou is professor of.finance in the School of Business and Economics at State.University of New York (Plattsburgh). He has published.25 books and is coeditor for the peer-reviewed Journal of Derivatives and Hedge Funds and editorial board member for the Journal of Wealth Management, Journal of Risk Management in Financial Institutions, and Brazilian Business Review.

Table of Contents

Editor	p. xv
Contributors	p. xvii
VaR Measurement
Calculating VaR for Hedge Funds	p. 3
Introduction	p. 4
Hedge Funds	p. 5
Value at Risk	p. 6
Data	p. 13
Results and Discussion	p. 14
Conclusion	p. 20
References	p. 20
Appendix: Strategic Decisions	p. 22
Efficient VaR: Using Past Forecast Performance to Generate Improved VaR Forecasts	p. 25
Introduction	p. 25
A Backtesting Framework	p. 27
Using Backtest Results to Recalibrate the Parameters of the VaR Model	p. 29
Some Examples	p. 31
Conclusion	p. 36
References	p. 37
Appendix	p. 38
Applying VaR to Hedge Fund Trading Strategies: Limitations and Challenges	p. 41
Introduction	p. 41
Background	p. 43
Analytical Approach	p. 44
Application Considerations	p. 46
Impact of VaR Control	p. 47
Short versus Long History for Setting VaR Risk Limits	p. 51
Implications	p. 53
Conclusion	p. 55
References	p. 56
Cash Flow at Risk: Linking Strategy and Finance	p. 59
Introduction	p. 59
A Process View of the Corporate Risk Management Function	p. 62
Value-Based Motives of Firm-Level Risk Management	p. 66
The Incompatibility of Simple Value at Risk with Corporate Risk Management	p. 70
Operationalizing CFaR	p. 72
Governance Implications	p. 78
Conclusion	p. 80
References	p. 81
Plausible Operational Value-at-Risk Calculations for Management Decision Making	p. 85
Introduction	p. 85
Operational Risk under Basel II	p. 86
Desirable Side Effects of Operational Risk Initiatives	p. 91
Toward Strategy-Enhancing Operational Risk Initiatives	p. 95
Employment of Real Option Techniques in Operational Risk Initiatives	p. 99
Conclusion	p. 102
References	p. 103
Value-at-Risk Performance Criterion: A Performance Measure for Evaluating Value-at-Risk Models	p. 105
Introduction	p. 106
Value-at-Risk Performance Criterion (VPC)	p. 107
Effects of Changing Volatility and Return Distribution	p. 109
Conclusion	p. 115
References	p. 119
Explaining Cross-Sectional Differences in Credit Default Swap Spreads: An Alternative Approach Using Value at Risk	p. 121
Introduction	p. 122
Estimation Methodology	p. 126
Data and Explanatory Variables	p. 128
Empirical Results	p. 131
Conclusion	p. 135
References	p. 135
Some Advanced Approaches to VaR Calculation and Measurement	p. 139
Introduction	p. 139
Parametric VaR and the Normal Distribution	p. 141
Using Historical Simulation to Compute VaR	p. 142
The Delta Method for Computing VaR	p. 145
The Monte Carlo Simulation	p. 147
The Bootstrapping Method	p. 149
Cornish-Fisher Expansion and VaR	p. 155
Value at Risk for a Distribution Other Than the Normal but Using a Normal Coefficient	p. 156
Copulas, Fourier's Transform, and the VaR	p. 157
Conclusion	p. 162
References	p. 163
Computational Aspects of Value at Risk	p. 167
Introduction	p. 168
Supercomputing Technologies	p. 169
Graphics Processing Unit Computing	p. 171
An Example	p. 174
Conclusion	p. 182
References	p. 182
Risk and Asset Management
Value-at-Risk-Based Stop-Loss Trading	p. 187
Introduction	p. 188
Stop-Loss Rules for Alternative Return Processes	p. 189
Some Well-known Strategies	p. 192
Conditional Autocorrelation: Threshold Autoregressive Models	p. 196
Conclusion	p. 202
References	p. 203
Appendix: Currency Universe and Data Availability	p. 205
Modeling Portfolio Risks with Time-Dependent Default Rates in Venture Capital	p. 207
Introduction	p. 208
Initial Model	p. 208
Risk Modeling with Time-Dependent Default Rates	p. 215
Empirical Evidence	p. 220
Conclusion	p. 226
References	p. 226
Risk Aggregation and Computation of Total Economic Capital	p. 229
Introduction	p. 229
Additive Approach	p. 232
Correlation-Based Square-Root Formula	p. 232
Top-Down Approach	p. 233
Bottom-Up Approach	p. 240
Conclusion	p. 241
References	p. 247
Value at Risk for High-Dimensional Portfolios: A Dynamic Grouped t-Copula Approach	p. 253
Introduction	p. 254
Dynamic Grouped t-Copula Modeling: Definition and Estimation	p. 256
Simulation Studies	p. 259
Empirical Analysis	p. 271
Conclusion	p. 277
References	p. 279
Appendix: List of Analyzed Stocks	p. 282
A Model to Measure Portfolio Risks in Venture Capital	p. 283
Introduction	p. 284
Toward a Risk Model in Venture Capital	p. 285
A Risk Model for Venture Capital	p. 290
Data Sample	p. 297
Empirical Evidence	p. 299
Conclusion	p. 308
References	p. 308
Risk Measures and Their Applications in Asset Management	p. 311
Introduction	p. 312
Risk Measures	p. 315
A Single-Period Portfolio Optimization Problem	p. 320
Elliptical World	p. 324
Modified Michelot Algorithm	p. 328
Computational Results	p. 331
Conclusion	p. 336
References	p. 336
Risk Evaluation of Sectors Traded at the ISE with VaR Analysis	p. 339
Introduction	p. 339
Value-at-Risk Comparison of Sectors Traded at the Istanbul Stock Exchange (ISE)	p. 343
Performance of VaR in Evaluating Risk	p. 350
Conclusion	p. 356
References	p. 357
Modeling
Aggregating and Combining Ratings	p. 361
Introduction	p. 362
Mathematical Background	p. 364
Aggregating Ratings	p. 365
Impact Studies	p. 367
Conclusion	p. 379
References	p. 381
Risk-Managing the Uncertainty in VaR Model Parameters	p. 385
The Subprime Crisis of 2008	p. 386
Parameter Uncertainty	p. 389
An Illustrative Example with Mean Uncertainty	p. 390
An Illustrative Example with Variance Uncertainty	p. 394
An Illustrative Example with Correlation Uncertainty	p. 396
Conclusion	p. 398
Acknowledgment	p. 400
References	p. 400
Structural Credit Modeling and Its Relationship To Market Value at Risk: An Australian Sectoral Perspective	p. 403
Introduction	p. 404
Structural Model	p. 406
Methodology	p. 407
Results	p. 410
Conclusion	p. 412
References	p. 412
Model Risk in VAR Calculations	p. 415
Introduction	p. 415
Sources of Model Risk	p. 416
Backtesting	p. 420
Bias versus Uncertainty	p. 422
Pivotal Quantile Estimates	p. 428
Applications	p. 432
Conclusion	p. 436
References	p. 436
Option Pricing with Constant and Time-Varying Volatility	p. 439
Introduction	p. 439
The Black-Scholes PDE	p. 441
Solution Methods	p. 444
What We Get and What We Do Not Get from Black-Scholes	p. 447
Seeking Sigma	p. 448
Historical Volatility	p. 449
GARCH(1,1)	p. 450
Heston's Volatility	p. 452
The Heston Valuation Equation	p. 453
Calibrating the Heston Parameters and Results	p. 457
Conclusion	p. 460
References	p. 460
Value at Risk under Heterogeneous Investment Horizons and Spatial Relations	p. 463
Introduction	p. 464
Methodological Issues	p. 466
Empirical Testing of Spatial Linkages	p. 471
Conclusion	p. 480
References	p. 481
How Investors Face Financial Risk Loss Aversion and Wealth Allocation with Two-Dimensional Individual Utility: A VaR Application	p. 485
Introduction	p. 486
Theoretical Model	p. 487
Application	p. 500
Conclusion	p. 510
References	p. 511
Index	p. 513
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