市場、信用とオペレーショナル・リスク:VaRのアプローチ<br>Understanding Market, Credit, and Operational Risk : The Value at Risk Approach

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市場、信用とオペレーショナル・リスク:VaRのアプローチ
Understanding Market, Credit, and Operational Risk : The Value at Risk Approach

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  • 製本 Hardcover:ハードカバー版/ページ数 250 p.
  • 言語 ENG
  • 商品コード 9780631227090
  • DDC分類 332.10681

基本説明

The book describes and critiques proprietary models, illustrating them with practical examples drawn from actual case studies, and explaining the logic behind the economics and statistics.

Full Description


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 with real--world case studies. * Features coverage of BIS bank capital requirements.

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 8 (2)
calculations
1.1.4 Inputs into VaR calculations 10 (3)
1.2 Diversification and VaR 13 (8)
1.2.1 Factors affecting portfolio 16 (1)
diversification
1.2.2 Decomposing volatility into 17 (1)
systematic and idiosyncratic risk
1.2.3 Diversification: Words of caution - 18 (3)
the case of long-term capital management
(LTCM)
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 23 (2)
changes
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 31 (3)
salvaged?
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  40 (8)
volatility
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 45 (1)
RiskMetrics 
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 62 (4)

Future Volatility

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 74 (8)

Results

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 93 (2)

optionality

3.1.5 "Delta normal" vs. full-revaluation 95 (2)

3.2 structured Monte Carlo, Stress Testing, 97 (13)

and scenario Analysis

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 106 (1)

simulation

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 119 (39)

Loans

4.1 Traditional Approaches to Credit Risk 120 (8)

Measurement

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 128 (4)

of credit risk measurement

4.2.2 Reduced form or intensity-based 132 (6)

models of credit risk measurement

4.2.3 Proprietary VaR models of credit risk 138 (1)

measurement

4.3 CreditMetrics 138 (13)

4.3.1 The distribution of an individual 138 (5)

loan's value

4.3.2 The value distribution for a 143 (18)

portfolio of loans

4.3.2.1 Calculating the correlation 144 (1)

between equity returns and industry

indices for each borrower in the loan

portfolio

4.3.2.2 Calculating the correlation 144 (1)

between borrower equity returns

4.3.2.3 Solving for joint migration 145 (2)

probabilities

4.3.2.4 Valuing each loan across the 147 (2)

entire credit migration spectrum

4.3.2.5 Calculating the mean and standard 149 (2)

deviation of the normal portfolio value

distribution

4.4 Algorithmics' Mark-to-Future 151 (2)

4.5 Summary 153 (2)

Appendix 4.1 CreditMetrics: Calculating 155 (3)

Credit VaR Using the Actual Distribution

5 Extending the VaR Approach to Operational 158 (42)

Risks

5.1 Top-Down Approaches to Operational Risk 161 (9)

Measurement

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 170 (15)

Measurement

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 195 (1)

hedging

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 203 (2)

risk

6.1.1.1 Measuring interest rate risk 203 (1)

6.1.1.2 Measuring foreign exchange rate 204 (1)

risk

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 209 (6)

credit risk

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 215 (3)

sheet credit risk

6.2.4 Assessment of the BIS regulatory 218 (3)

models of credit risk

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