Understanding Market, Credit, and Operational Risk : The Value at Risk Approach
Linda. Allen
Bok Engelsk 2009 · Electronic books.
| Annen tittel | |
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| Utgitt | Hoboken : : Wiley, , 2009.
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| Omfang | 1 online resource (312 p.)
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| Opplysninger | Description based upon print version of record.. - LIST OF FIGURES; LIST OF TABLES; PREFACE; LIST OF ABBREVIATIONS; 1 INTRODUCTION TO VALUE AT RISK (VaR); 1.1 ECONOMICS UNDERLYING VaR MEASUREMENT; 1.1.1 What is VaR?; 1.1.2 Calculating VaR; 1.1.3 The assumptions behind VaR calculations; 1.1.4 Inputs into VaR calculations; 1.2 DIVERSIFICATION AND VaR; 1.2.1 Factors affecting portfolio diversification; 1.2.2 Decomposing volatility into systematic and idiosyncratic risk; 1.2.3 Diversification: Words of caution - the case of long-term capital management (LTCM); 2 QUANTIFYING VOLATILITY IN VaR MODELS; 2.1 THE STOCHASTIC BEHAVIOR OF RETURNS. - 2.1.1 Revisiting the assumptions2.1.2 The distribution of interest rate changes; 2.1.3 Fat tails; 2.1.4 Explaining fat tails; 2.1.5 Effects of volatility changes; 2.1.6 Can (conditional) normality be salvaged?; 2.1.7 Normality cannot be salvaged; 2.2 VaR ESTIMATION APPROACHES; 2.2.1 Cyclical volatility; 2.2.2 Historical standard deviation; 2.2.3 Implementation considerations; 2.2.4 Exponential smoothing - RiskMetricsTM volatility; 2.2.4.1 The optimal smoother lambda; 2.2.4.2 Adaptive volatility estimation; 2.2.4.3 The empirical performance of RiskMetricsTM; 2.2.4.4 GARCH. - 2.2.5 Nonparametric volatility forecasting2.2.5.1 Historical simulation; 2.2.5.2 Multivariate density estimation; 2.2.6 A comparison of methods; 2.2.7 The hybrid approach; 2.3 RETURN AGGREGATION AND VaR; 2.4 IMPLIED VOLATILITY AS A PREDICTOR OF FUTURE VOLATILITY; 2.5 LONG HORIZON VOLATILITY AND VaR; 2.6 MEAN REVERSION AND LONG HORIZON VOLATILITY; 2.7 CORRELATION MEASUREMENT; 2.8 SUMMARY; APPENDIX 2.1 BACKTESTING METHODOLOGY AND RESULTS; 3 PUTTING VaR TO WORK; 3.1 THE VaR OF DERIVATIVES - PRELIMINARIES; 3.1.1 Linear derivatives; 3.1.2 Nonlinear derivatives. - 3.1.3 Approximating the VaR of derivatives3.1.4 Fixed income securities with embedded optionality; 3.1.5 "Delta normal" vs. full-revaluation; 3.2 STRUCTURED MONTE CARLO, STRESS TESTING, AND SCENARIO ANALYSIS; 3.2.1 Motivation; 3.2.2 Structured Monte Carlo; 3.2.3 Scenario analysis; 3.2.3.1 Correlation breakdown; 3.2.3.2 Generating reasonable stress; 3.2.3.3 Stress testing in practice; 3.2.3.4 Stress testing and historical simulation; 3.2.3.5 Asset concentration; 3.3 WORST-CASE SCENARIO (WCS); 3.3.1 WCS vs. VaR; 3.3.2 A comparison of VaR to WCS; 3.3.3 Extensions; 3.4 SUMMARY. - 4.3.2.1 Calculating the correlation between equity returns and industry indices for each borrower in the loan portfolio. - APPENDIX 3.1 DURATION4 EXTENDING THE VaR APPROACH TO NON-TRADABLE LOANS; 4.1 TRADITIONAL APPROACHES TO CREDIT RISK MEASUREMENT; 4.1.1 Expert systems; 4.1.2 Rating systems; 4.1.3 Credit scoring models; 4.2 THEORETICAL UNDERPINNINGS: TWO APPROACHES; 4.2.1 Options-theoretic structural models of credit risk measurement; 4.2.2 Reduced form or intensity-based models of credit risk measurement; 4.2.3 Proprietary VaR models of credit risk measurement; 4.3 CREDITMETRICS; 4.3.1 The distribution of an individual loan's value; 4.3.2 The value distribution for a portfolio of loans. - 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. Applies the Value at Risk approach to market, credit, and operational risk measurement. Illustrates models w
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| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 0631227091
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