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.
Greg N. Gregoriou
is professor of finance in the School of Business and
Economics at State University of New York (Plattsburgh). He has published twenty-five
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 xv
Contributors xvii
Part 1 VaR Measurement
Chapter 1 Calculating VaR for Hedge Funds Monica Billio Mila Getmansky
Loriana Pelizzon 3
Introduction 4
Hedge Funds 5
Value at Risk 6
Data 13
Results and Discussion 14
Conclusion 20
References 20
Appendix Strategic Decisions 22
Chapter 2 Efficient VaR: Using Past Forecast Performance to Generate Improved VaR
Forecasts Kevin Dowd Carlos Blanco 25
Introduction 25
A Backtesting Framework 27
Using Backtest Results to Recalibrate the Parameters of the VaR Model 29
Some Examples 31
Conclusion 36
References 37
Appendix 38
Chapter 3 Applying VaR to Hedge Fund Trading Strategies: Limitations and
Challenges R. McFall Lamm, Jr. 41
Introduction 41
Background 43
Analytical Approach 44
Application Considerations 46
Impact of VaR Control 47
Short versus Long History for Setting VaR Risk Limits 51
Implications 53
Conclusion 55
References 56
Chapter 4 Cash Flow at Risk: Linking Strategy and Finance Ulrich Hommel
59
Introduction 59
A Process View of the Corporate Risk Management Function 62
Value-Based Motives of Firm-Level Risk Management 66
The Incompatibility of Simple Value at Risk with Corporate Risk Management 70
Operationalizing CFaR 72
Governance Implications 78
Conclusion 80
References 81
Chapter 5 Plausible Operational Value-at-Risk Calculations for Management Decision
Making Wilhelm Kross Ulrich Hommel Martin Wiethuechter 85
Introduction 85
Operational Risk under Basel II 86
Desirable Side Effects of Operational Risk Initiatives 91
Toward Strategy-Enhancing Operational Risk Initiatives 95
Employment of Real Option Techniques in Operational RiskInitiatives 99
Conclusion 102
References 103
Chapter 6 Value-at-Risk Performance Criterion: A Performance Measure for
Evaluating Value-at-Risk Models Zeno Adams Roland Fuss 105
Introduction 106
Value-at-Risk Performance Criterion (VPC) 107
Effects of Changing Volatility and Return Distribution 109
Conclusion 115
References 119
Chapter 7 Explaining Cross-Sectional Differences in Credit Default Swap Spreads:
An Alternative Approach Using Value at Risk Bastian Breitenfellner Niklas Wagner
121
Introduction 122
Estimation Methodology 126
Data and Explanatory Variables 128
Empirical Results 131
Conclusion 135
References 135
Chapter 8 Some Advanced Approaches to VaR Calculation and Measurement Francois-Eric
Racicot Raymond Theoret 139
Introduction 139
Parametric VaR and the Normal Distribution 141
Using Historical Simulation to Compute VaR 142
The Delta Method for Computing VaR 145
The Monte Carlo Simulation 147
The Bootstrapping Method 149
Cornish-Fisher Expansion and VaR 155
Value at Risk for a Distribution Other Than the Normal but Using a Normal Coefficient 156
Copulas, Fourier's Transform, and the VaR 157
Conclusion 162
References 163
Chapter 9 Computational Aspects of Value at Risk German Navarro Ignacio
Olmeda 167
Introduction 168
Supercomputing Technologies 169
Graphics Processing Unit Computing 171
An Example 174
Conclusion 182
References 182
Part 2 Risk and Asset Management
Chapter 10 Value-at-Risk-Based Stop-Loss Trading Bernd Scherer 187
Introduction 188
Stop-Loss Rules for Alternative Return Processes 189
Some Well-known Strategies 192
Conditional Autocorrelation: Threshold Autoregressive Models 196
Conclusion 202
References 203
Appendix: Currency Universe and Data Availability 205
Chapter 11 Modeling Portfolio Risks with Time-Dependent Default Rates in Venture
Capital Andreas Kemmerer Jan Rietzschel Henry Schoenball 207
Introduction 208
Initial Model 208
Risk Modeling with Time-Dependent Default Rates 215
Empirical Evidence 220
Conclusion 226
References 226
Chapter 12 Risk Aggregation and Computation of Total Economic Capital
Peter Grundke 229
Introduction 229
Additive Approach 232
Correlation-Based Square-Root Formula 232
Top-Down Approach 233
Bottom-Up Approach 240
Conclusion 241
References 247
Chapter 13 Value at Risk for High-Dimensional Portfolios: A Dynamic Grouped
t-Copula Approach Dean Fantazzini 253
Introduction 254
Dynamic Grouped t-Copula Modeling: Definition and Estimation 256
Simulation Studies 259
Empirical Analysis 271
Conclusion 277
References 279
Appendix: List of Analyzed Stocks 282
Chapter 14 A Model to Measure Portfolio Risks in Venture Capital Andreas
Kemmerer 283
Introduction 284
Toward a Risk Model in Venture Capital 285
A Risk Model for Venture Capital 290
Data Sample 297
Empirical Evidence 299
Conclusion 308
References 308
Chapter 15 Risk Measures and Their Applications in Asset Management S.
Ilker Birbil Hans Frenk Bahar Kaynar Nilay Noyan 311
Introduction 312
Risk Measures 315
A Single-Period Portfolio Optimization Problem 320
Elliptical World 324
Modified Michelot Algorithm 328
Computational Results 331
Conclusion 336
References 336
Chapter 16 Risk Evaluation of Sectors Traded at the ISE with VaR Analysis Mehmet
Orhan Gokhan Karaahmet 339
Introduction 339
Value-at-Risk Comparison of Sectors Traded at the Istanbul Stock Exchange (ISE) 343
Performance of VaR in Evaluating Risk 350
Conclusion 356
References 357
Part 3 Modeling
Chapter 17 Aggregating and Combining Ratings Rafael Weib<$$$>bach
Frederik Kramer Claudia Lawrenz 361
Introduction 362
Mathematical Background 364
Aggregating Ratings 365
Impact Studies 367
Conclusion 379
References 381
Chapter 18 Risk-Managing the Uncertainty in VaR Model Parameters Jason C.
Hsu Vitali Kalesnik 385
The Subprime Crisis of 2008 386
Parameter Uncertainty 389
An Illustrative Example with Mean Uncertainty 390
An Illustrative Example with Variance Uncertainty 394
An Illustrative Example with Correlation Uncertainty 396
Conclusion 398
Acknowledgment 400
References 400
Chapter 19 Structural Credit Modeling and Its Relationship To Market Value at
Risk: An Australian Sectoral Perspective David E. Allen Robert Powell 403
Introduction 404
Structural Model 406
Methodology 407
Results 410
Conclusion 412
References 412
Chapter 20 Model Risk in VAR Calculations Peter Schaller 415
Introduction 415
Sources of Model Risk 416
Backtesting 420
Bias versus Uncertainty 422
Pivotal Quantile Estimates 428
Applications 432
Conclusion 436
References 436
Chapter 21 Option Pricing with Constant and Time-Varying Volatility Willi
Semmler Karim M. Youssef 439
Introduction 439
The Black-Scholes PDE 441
Solution Methods 444
What We Get and What We Do Not Get from Black-Scholes 447
Seeking Sigma 448
Historical Volatility 449
GARCH(1,1) 450
Heston's Volatility 452
The Heston Valuation Equation 453
Calibrating the Heston Parameters and Results 457
Conclusion 460
References 460
Chapter 22 Value at Risk under Heterogeneous Investment Horizons and Spatial
Relations Viviana Fernandez 463
Introduction 464
Methodological Issues 466
Empirical Testing of Spatial Linkages 471
Conclusion 480
References 481
Chapter 23 How Investors Face Financial Risk Loss Aversion and Wealth Allocation
with Two-Dimensional Individual Utility: A VaR Application Erick W. Rengifo
Emanuela Trifan 485
Introduction 486
Theoretical Model 487
Application 500
Conclusion 510
References 511
Index 513
624 pages, Hardcover
