Quantitative risk management : concepts, techniques, and tools

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Quantitative risk management : concepts, techniques, and tools / Alexander J. McNeil, Rüdiger Frey, Paul Embrechts

資料種別:

図書

出版情報:

Princeton, N.J. : Princeton University Press, c2005

形態:

xv, 538 p. ; 24 cm

シリーズ名:

Princeton series in finance <BA54784338>

著者名:

ISBN:

9780691122557 [0691122555] (: cloth, alk. paper)

書誌ID:

BA73827165

子書誌情報

フルテキスト

所蔵情報

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書誌詳細

注記:

Includes bibliographical references (p. [503]-527) and index

主題:

Finance--Mathematical models; Insurance--Mathematical models; Mathematical statistics; Risk management--Mathematical models

言語:

英語

目次情報:

Preface

Risk in Perspective / 1：

Risk / 1.1：

Risk and Randomness / 1.1.1：

Financial Risk / 1.1.2：

Measurement and Management / 1.1.3：

A Brief History of Risk Management / 1.2：

From Babylon to Wall Street / 1.2.1：

The Road to Regulation / 1.2.2：

The New Regulatory Framework / 1.3：

Basel II / 1.3.1：

Solvency 2 / 1.3.2：

Why Manage Financial Risk? / 1.4：

A Societal View / 1.4.1：

The Shareholder's View / 1.4.2：

Economic Capital / 1.4.3：

Quantitative Risk Management / 1.5：

The Nature of the Challenge / 1.5.1：

QRM for the Future / 1.5.2：

Basic Concepts in Risk Management / 2：

Risk Factors and Loss Distributions / 2.1：

General Definitions / 2.1.1：

Conditional and Unconditional Loss Distribution / 2.1.2：

Mapping of Risks: Some Examples / 2.1.3：

Risk Measurement / 2.2：

Approaches to Risk Measurement / 2.2.1：

Value-at-Risk / 2.2.2：

Further Comments on VaR / 2.2.3：

Other Risk Measures Based on Loss Distributions / 2.2.4：

Standard Methods for Market Risks / 2.3：

Variance-Covariance Method / 2.3.1：

Historical Simulation / 2.3.2：

Monte Carlo / 2.3.3：

Losses over Several Periods and Scaling / 2.3.4：

Backtesting / 2.3.5：

An Illustrative Example / 2.3.6：

Multivariate Models / 3：

Basics of Multivariate Modelling / 3.1：

Random Vectors and Their Distributions / 3.1.1：

Standard Estimators of Covariance and Correlation / 3.1.2：

The Multivariate Normal Distribution / 3.1.3：

Testing Normality and Multivariate Normality / 3.1.4：

Normal Mixture Distributions / 3.2：

Normal Variance Mixtures / 3.2.1：

Normal Mean-Variance Mixtures / 3.2.2：

Generalized Hyperbolic Distributions / 3.2.3：

Fitting Generalized Hyperbolic Distributions to Data / 3.2.4：

Empirical Examples / 3.2.5：

Spherical and Elliptical Distributions / 3.3：

Spherical Distributions / 3.3.1：

Elliptical Distributions / 3.3.2：

Properties of Elliptical Distributions / 3.3.3：

Estimating Dispersion and Correlation / 3.3.4：

Testing for Elliptical Symmetry / 3.3.5：

Dimension Reduction Techniques / 3.4：

Factor Models / 3.4.1：

Statistical Calibration Strategies / 3.4.2：

Regression Analysis of Factor Models / 3.4.3：

Principal Component Analysis / 3.4.4：

Financial Time Series / 4：

Empirical Analyses of Financial Time Series / 4.1：

Stylized Facts / 4.1.1：

Multivariate Stylized Facts / 4.1.2：

Fundamentals of Time Series Analysis / 4.2：

Basic Definitions / 4.2.1：

ARMA Processes / 4.2.2：

Analysis in the Time Domain / 4.2.3：

Statistical Analysis of Time Series / 4.2.4：

Prediction / 4.2.5：

GARCH Models for Changing Volatility / 4.3：

ARCH Processes / 4.3.1：

GARCH Processes / 4.3.2：

Simple Extensions of the GARCH Model / 4.3.3：

Fitting GARCH Models to Data / 4.3.4：

Volatility Models and Risk Estimation / 4.4：

Volatility Forecasting / 4.4.1：

Conditional Risk Measurement / 4.4.2：

Fundamentals of Multivariate Time Series / 4.4.3：

Multivariate ARMA Processes / 4.5.1：

Multivariate GARCH Processes / 4.6：

General Structure of Models / 4.6.1：

Models for Conditional Correlation / 4.6.2：

Models for Conditional Covariance / 4.6.3：

Fitting Multivariate GARCH Models / 4.6.4：

Dimension Reduction in MGARCH / 4.6.5：

MGARCH and Conditional Risk Measurement / 4.6.6：

Copulas and Dependence / 5：

Copulas / 5.1：

Basic Properties / 5.1.1：

Examples of Copulas / 5.1.2：

Meta Distributions / 5.1.3：

Simulation of Copulas and Meta Distributions / 5.1.4：

Further Properties of Copulas / 5.1.5：

Perfect Dependence / 5.1.6：

Dependence Measures / 5.2：

Linear Correlation / 5.2.1：

Rank Correlation / 5.2.2：

Coefficients of Tail Dependence / 5.2.3：

Normal Mixture Copulas / 5.3：

Tail Dependence / 5.3.1：

Rank Correlations / 5.3.2：

Skewed Normal Mixture Copulas / 5.3.3：

Grouped Normal Mixture Copulas / 5.3.4：

Archimedean Copulas / 5.4：

Bivariate Archimedean Copulas / 5.4.1：

Multivariate Archimedean Copulas / 5.4.2：

Non-exchangeable Archimedean Copulas / 5.4.3：

Fitting Copulas to Data / 5.5：

Method-of-Moments using Rank Correlation / 5.5.1：

Forming a Pseudo-Sample from the Copula / 5.5.2：

Maximum Likelihood Estimation / 5.5.3：

Aggregate Risk / 6：

Coherent Measures of Risk / 6.1：

The Axioms of Coherence / 6.1.1：

Coherent Risk Measures Based on Loss Distributions / 6.1.2：

Coherent Risk Measures as Generalized Scenarios / 6.1.4：

Mean-VaR Portfolio Optimization / 6.1.5：

Bounds for Aggregate Risks / 6.2：

The General Frechet Problem / 6.2.1：

The Case of VaR / 6.2.2：

Capital Allocation / 6.3：

The Allocation Problem / 6.3.1：

The Euler Principle and Examples / 6.3.2：

Economic Justification of the Euler Principle / 6.3.3：

Extreme Value Theory / 7：

Maxima / 7.1：

Generalized Extreme Value Distribution / 7.1.1：

Maximum Domains of Attraction / 7.1.2：

Maxima of Strictly Stationary Time Series / 7.1.3：

The Block Maxima Method / 7.1.4：

Threshold Exceedances / 7.2：

Generalized Pareto Distribution / 7.2.1：

Modelling Excess Losses / 7.2.2：

Modelling Tails and Measures of Tail Risk / 7.2.3：

The Hill Method / 7.2.4：

Simulation Study of EVT Quantile Estimators / 7.2.5：

Conditional EVT for Financial Time Series / 7.2.6：

Tails of Specific Models / 7.3：

Domain of Attraction of Frechet Distribution / 7.3.1：

Domain of Attraction of Gumbel Distribution / 7.3.2：

Mixture Models / 7.3.3：

Point Process Models / 7.4：

Threshold Exceedances for Strict White Noise / 7.4.1：

The POT Model / 7.4.2：

Self-Exciting Processes / 7.4.3：

A Self-Exciting POT Model / 7.4.4：

Multivariate Maxima / 7.5：

Multivariate Extreme Value Copulas / 7.5.1：

Copulas for Multivariate Minima / 7.5.2：

Copula Domains of Attraction / 7.5.3：

Modelling Multivariate Block Maxima / 7.5.4：

Multivariate Threshold Exceedances / 7.6：

Threshold Models Using EV Copulas / 7.6.1：

Fitting a Multivariate Tail Model / 7.6.2：

Threshold Copulas and Their Limits / 7.6.3：

Credit Risk Management / 8：

Introduction to Credit Risk Modelling / 8.1：

Credit Risk Models / 8.1.1：

Structural Models of Default / 8.1.2：

The Merton Model / 8.2.1：

Pricing in Merton's Model / 8.2.2：

The KMV Model / 8.2.3：

Models Based on Credit Migration / 8.2.4：

Multivariate Firm-Value Models / 8.2.5：

Threshold Models / 8.3：

Notation for One-Period Portfolio Models / 8.3.1：

Threshold Models and Copulas / 8.3.2：

Industry Examples / 8.3.3：

Models Based on Alternative Copulas / 8.3.4：

Model Risk Issues / 8.3.5：

The Mixture Model Approach / 8.4：

One-Factor Bernoulli Mixture Models / 8.4.1：

CreditRisk+ / 8.4.2：

Asymptotics for Large Portfolios / 8.4.3：

Threshold Models as Mixture Models / 8.4.4：

Model-Theoretic Aspects of Basel II / 8.4.5：

Monte Carlo Methods / 8.4.6：

Basics of Importance Sampling / 8.5.1：

Application to Bernoulli-Mixture Models / 8.5.2：

Statistical Inference for Mixture Models / 8.6：

Motivation / 8.6.1：

Exchangeable Bernoulli-Mixture Models / 8.6.2：

Mixture Models as GLMMs / 8.6.3：

One-Factor Model with Rating Effect / 8.6.4：

Dynamic Credit Risk Models / 9：

Credit Derivatives / 9.1：

Overview / 9.1.1：

Single-Name Credit Derivatives / 9.1.2：

Portfolio Credit Derivatives / 9.1.3：

Mathematical Tools / 9.2：

Random Times and Hazard Rates / 9.2.1：

Modelling Additional Information / 9.2.2：

Doubly Stochastic Random Times / 9.2.3：

Financial and Actuarial Pricing of Credit Risk / 9.3：

Physical and Risk-Neutral Probability Measure / 9.3.1：

Risk-Neutral Pricing and Market Completeness / 9.3.2：

Martingale Modelling / 9.3.3：

The Actuarial Approach to Credit Risk Pricing / 9.3.4：

Pricing with Doubly Stochastic Default Times / 9.4：

Recovery Payments of Corporate Bonds / 9.4.1：

The Model / 9.4.2：

Pricing Formulas / 9.4.3：

Applications / 9.4.4：

Affine Models / 9.5：

Basic Results / 9.5.1：

The CIR Square-Root Diffusion / 9.5.2：

Extensions / 9.5.3：

Conditionally Independent Defaults / 9.6：

Reduced-Form Models for Portfolio Credit Risk / 9.6.1：

Conditionally Independent Default Times / 9.6.2：

Examples and Applications / 9.6.3：

Copula Models / 9.7：

Definition and General Properties / 9.7.1：

Factor Copula Models / 9.7.2：

Default Contagion in Reduced-Form Models / 9.8：

Default Contagion and Default Dependence / 9.8.1：

Information-Based Default Contagion / 9.8.2：

Interacting Intensities / 9.8.3：

Operational Risk and Insurance Analytics / 10：

Operational Risk in Perspective / 10.1：

A New Risk Class / 10.1.1：

The Elementary Approaches / 10.1.2：

Advanced Measurement Approaches / 10.1.3：

Operational Loss Data / 10.1.4：

Elements of Insurance Analytics / 10.2：

The Case for Acturaial Methodology / 10.2.1：

The Total Loss Amount / 10.2.2：

Approximations and Panjer Recursion / 10.2.3：

Poisson Mixtures / 10.2.4：

Tails of Aggregate Loss Distributions / 10.2.5：

The Homogeneous Poisson Process / 10.2.6：

Processes Related to the Poisson Process / 10.2.7：

Appendix

Miscellaneous Definitions and Results / A.1：

Type of Distribution / A.1.1：

Generalized Inverses and Quantiles / A.1.2：

Karamata's Theorem / A.1.3：

Probability Distributions / A.2：

Beta / A.2.1：

Exponential / A.2.2：

F / A.2.3：

Gamma / A.2.4：

Generalized Inverse Gaussian / A.2.5：

Inverse Gamma / A.2.6：

Negative Binomial / A.2.7：

Pareto / A.2.8：

Stable / A.2.9：

Likelihood Inference / A.3：

Maximum Likelihood Estimators / A.3.1：

Asymptotic Results: Scalar Parameter / A.3.2：

Asymptotic Results: Vector of Parameters / A.3.3：

Wald Test and Confidence Intervals / A.3.4：

Likelihood Ratio Test and Confidence Intervals / A.3.5：

Akaike Information Criterion / A.3.6：

References

Index

Preface

Risk in Perspective / 1：

Risk / 1.1：

Risk and Randomness / 1.1.1：

Financial Risk / 1.1.2：

Measurement and Management / 1.1.3：

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