Quantitative Risk Management: Concepts, Techniques and Tools

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Quantitative Risk Management: Concepts, Techniques and Tools
之前有人传过零散章节，这个是全的，作者网页上有开发的Splus 和R的程序包，并有数据，可供学习下载，并附赠一个
简要版的400多页的slides，制作精美，供学习参考。
CHAPTER 1: Risk in Perspective 1
1.1 Risk 1
1.1.1 Risk and Randomness 1
1.1.2 Financial Risk 2
1.1.3 Measurement and Management 3
1.2 A Brief History of Risk Management 5
1.2.1 From Babylon to Wall Street 5
1.2.2 The Road to Regulation 8
1.3 The New Regulatory Framework 10
1.3.1 Basel II 10
1.3.2 Solvency 2 13
1.4 Why Manage Financial Risk? 15
1.4.1 A Societal View 15
1.4.2 The Shareholder's View 16
1.4.3 Economic Capital 18
1.5 Quantitative Risk Management 19
1.5.1 The Nature of the Challenge 19
1.5.2 QRM for the Future 22
CHAPTER 2: Basic Concepts in Risk Management 25
2.1 Risk Factors and Loss Distributions 25
2.1.1 General Definitions 25
2.1.2 Conditional and Unconditional Loss Distribution 28
2.1.3 Mapping of Risks:Some Examples 29
2.2 Risk Measurement 34
2.2.1 Approaches to Risk Measurement 34
2.2.2 Value-at-Risk 37
2.2.3 Further Comments on VaR 40
2.2.4 Other Risk Measures Based on Loss Distributions 43
2.3 Standard Methods for Market Risks 48
2.3.1 Variance -Covariance Method 48
2.3.2 Historical Simulation 50
2.3.3 Monte Carlo 52
2.3.4 Losses over Several Periods and Scaling 53
2.3.5 Backtesting 55
2.3.6 An Illustrative Example 55
CHAPTER 3: Multivariate Models 61
3.1 Basics of Multivariate Modelling 61
3.1.1 Random Vectors and Their Distributions 62
3.1.2 Standard Estimators of Covariance and Correlation 64
3.1.3 The Multivariate Normal Distribution 66
3.1.4 Testing Normality and Multivariate Normality 68
3.2 Normal Mixture Distributions 73
3.2.1 Normal Variance Mixtures 73
3.2.2 Normal Mean-Variance Mixtures 77
3.2.3 Generalized Hyperbolic Distributions 78
3.2.4 Fitting Generalized Hyperbolic Distributions to Data 81
3.2.5 Empirical Examples 84
3.3 Spherical and Elliptical Distributions 89
3.3.1 Spherical Distributions 89
3.3.2 Elliptical Distributions 93
3.3.3 Properties of Elliptical Distributions 95
3.3.4 Estimating Dispersion and Correlation 96
3.3.5 Testing for Elliptical Symmetry 99
3.4 Dimension Reduction Techniques 103
3.4.1 Factor Models 103
3.4.2 Statistical Calibration Strategies 105
3.4.3 Regression Analysis of Factor Models 106
3.4.4 Principal Component Analysis 109
CHAPTER 4: Financial Time Series 116
4.1 Empirical Analyses of Financial Time Series 117
4.1.1 Stylized Facts 117
4.1.2 Multivariate Stylized Facts 123
4.2 Fundamentals of Time Series Analysis 125
4.2.1 Basic Definitions 125
4.2.2 ARMA Processes 128
4.2.3 Analysis in the Time Domain 132
4.2.4 Statistical Analysis of Time Series 134
4.2.5 Prediction 136
4.3 GARCH Models for Changing Volatility 139
4.3.1 ARCH Processes 139
4.3.2 GARCH Processes 145
4.3.3 Simple Extensions of the GARCH Model 148
4.3.4 Fitting GARCH Models to Data 150
4.4 Volatility Models and Risk Estimation 158
4.4.1 Volatility Forecasting 158
4.4.2 Conditional Risk Measurement 160
4.4.3 Backtesting 162
4.5 Fundamentals of Multivariate Time Series 164
4.5.1 Basic Definitions 164
4.5.2 Analysis in the Time Domain 166
4.5.3 Multivariate ARMA Processes 168
4.6 Multivariate GARCH Processes 170
4.6.1 General Structure of Models 170
4.6.2 Models for Conditional Correlation 172
4.6.3 Models for Conditional Covariance 175
4.6.4 Fitting Multivariate GARCH Models 178
4.6.5 Dimension Reduction in MGARCH 179
4.6.6 MGARCH and Conditional Risk Measurement 182
CHAPTER 5: Copulas and Dependence 184
5.1 Copulas 184
5.1.1 Basic Properties 185
5.1.2 Examples of Copulas 189
5.1.3 Meta Distributions 192
5.1.4 Simulation of Copulas and Meta Distributions 193
5.1.5 Further Properties of Copulas 195
5.1.6 Perfect Dependence 199
5.2 Dependence Measures 201
5.2.1 Linear Correlation 201
5.2.2 Rank Correlation 206
5.2.3 Coefficients of Tail Dependence 208
5.3 Normal Mixture Copulas 210
5.3.1 Tail Dependence 210
5.3.2 Rank Correlations 215
5.3.3 Skewed Normal Mixture Copulas 217
5.3.4 Grouped Normal Mixture Copulas 218
5.4 Archimedean Copulas 220
5.4.1 Bivariate Archimedean Copulas 220
5.4.2 Multivariate Archimedean Copulas 222
5.4.3 Non-exchangeable Archimedean Copulas 224
5.5 Fitting Copulas to Data 228
5.5.1 Method-of-Moments using Rank Correlation 229
5.5.2 Forming a Pseudo-Sample from the Copula 232
5.5.3 Maximum Likelihood Estimation 234
CHAPTER 6: Aggregate Risk 238
6.1 Coherent Measures of Risk 238
6.1.1 The Axioms of Coherence 238
6.1.2 Value-at-Risk 241
6.1.3 Coherent Risk Measures Based on Loss Distributions 243
6.1.4 Coherent Risk Measures as Generalized Scenarios 244
6.1.5 Mean-VaR Portfolio Optimization 246
6.2 Bounds for Aggregate Risks 248
6.2.1 The General Fréchet Problem 248
6.2.2 The Case of VaR 250
6.3 Capital Allocation 256
6.3.1 The Allocation Problem 256
6.3.2 The Euler Principle and Examples 257
6.3.3 Economic Justification of the Euler Principle 261
CHAPTER 7: Extreme Value Theory 264

CHAPTER 8: Credit Risk Management 327

CHAPTER 9: Dynamic Credit Risk Models 385
9

CHAPTER 10: Operational Risk and Insurance Analytics 463

Appendix 494

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