Quantitative risk management : concepts, techniques and tools /

Quantitative risk management : concepts, techniques and tools /

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Bibliographic Details
Author / Creator:McNeil, Alexander J., 1967-
Imprint:Princeton, N.J. : Princeton University Press,cc2005.
Description:xv, 538 p. : ill. ; 24 cm.
Language:English
Series:Princeton series in finance
Subject:
Risk management -- Mathematical models.
Finance -- Mathematical models.
Insurance -- Mathematical models.
Mathematical statistics.
Finance -- Mathematical models.
Insurance -- Mathematical models.
Mathematical statistics.
Risk management -- Mathematical models.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5753213
Hidden Bibliographic Details
Other authors / contributors:Frey, Rüdiger.
Embrechts, Paul, 1953-
ISBN:0691122555 (cloth : alk. paper)
Notes:Includes bibliographical references (p. [503]-527) and index.
Table of Contents:
  • Preface
  • 1. Risk in Perspective
  • 1.1. Risk
  • 1.1.1. Risk and Randomness
  • 1.1.2. Financial Risk
  • 1.1.3. Measurement and Management
  • 1.2. A Brief History of Risk Management
  • 1.2.1. From Babylon to Wall Street
  • 1.2.2. The Road to Regulation
  • 1.3. The New Regulatory Framework
  • 1.3.1. Basel II
  • 1.3.2. Solvency 2
  • 1.4. Why Manage Financial Risk?
  • 1.4.1. A Societal View
  • 1.4.2. The Shareholder's View
  • 1.4.3. Economic Capital
  • 1.5. Quantitative Risk Management
  • 1.5.1. The Nature of the Challenge
  • 1.5.2. QRM for the Future
  • 2. Basic Concepts in Risk Management
  • 2.1. Risk Factors and Loss Distributions
  • 2.1.1. General Definitions
  • 2.1.2. Conditional and Unconditional Loss Distribution
  • 2.1.3. Mapping of Risks: Some Examples
  • 2.2. Risk Measurement
  • 2.2.1. Approaches to Risk Measurement
  • 2.2.2. Value-at-Risk
  • 2.2.3. Further Comments on VaR
  • 2.2.4. Other Risk Measures Based on Loss Distributions
  • 2.3. Standard Methods for Market Risks
  • 2.3.1. Variance-Covariance Method
  • 2.3.2. Historical Simulation
  • 2.3.3. Monte Carlo
  • 2.3.4. Losses over Several Periods and Scaling
  • 2.3.5. Backtesting
  • 2.3.6. An Illustrative Example
  • 3. Multivariate Models
  • 3.1. Basics of Multivariate Modelling
  • 3.1.1. Random Vectors and Their Distributions
  • 3.1.2. Standard Estimators of Covariance and Correlation
  • 3.1.3. The Multivariate Normal Distribution
  • 3.1.4. Testing Normality and Multivariate Normality
  • 3.2. Normal Mixture Distributions
  • 3.2.1. Normal Variance Mixtures
  • 3.2.2. Normal Mean-Variance Mixtures
  • 3.2.3. Generalized Hyperbolic Distributions
  • 3.2.4. Fitting Generalized Hyperbolic Distributions to Data
  • 3.2.5. Empirical Examples
  • 3.3. Spherical and Elliptical Distributions
  • 3.3.1. Spherical Distributions
  • 3.3.2. Elliptical Distributions
  • 3.3.3. Properties of Elliptical Distributions
  • 3.3.4. Estimating Dispersion and Correlation
  • 3.3.5. Testing for Elliptical Symmetry
  • 3.4. Dimension Reduction Techniques
  • 3.4.1. Factor Models
  • 3.4.2. Statistical Calibration Strategies
  • 3.4.3. Regression Analysis of Factor Models
  • 3.4.4. Principal Component Analysis
  • 4. Financial Time Series
  • 4.1. Empirical Analyses of Financial Time Series
  • 4.1.1. Stylized Facts
  • 4.1.2. Multivariate Stylized Facts
  • 4.2. Fundamentals of Time Series Analysis
  • 4.2.1. Basic Definitions
  • 4.2.2. ARMA Processes
  • 4.2.3. Analysis in the Time Domain
  • 4.2.4. Statistical Analysis of Time Series
  • 4.2.5. Prediction
  • 4.3. GARCH Models for Changing Volatility
  • 4.3.1. ARCH Processes
  • 4.3.2. GARCH Processes
  • 4.3.3. Simple Extensions of the GARCH Model
  • 4.3.4. Fitting GARCH Models to Data
  • 4.4. Volatility Models and Risk Estimation
  • 4.4.1. Volatility Forecasting
  • 4.4.2. Conditional Risk Measurement
  • 4.4.3. Backtesting
  • 4.5. Fundamentals of Multivariate Time Series
  • 4.5.1. Basic Definitions
  • 4.5.2. Analysis in the Time Domain
  • 4.5.3. Multivariate ARMA Processes
  • 4.6. Multivariate GARCH Processes
  • 4.6.1. General Structure of Models
  • 4.6.2. Models for Conditional Correlation
  • 4.6.3. Models for Conditional Covariance
  • 4.6.4. Fitting Multivariate GARCH Models
  • 4.6.5. Dimension Reduction in MGARCH
  • 4.6.6. MGARCH and Conditional Risk Measurement
  • 5. Copulas and Dependence
  • 5.1. Copulas
  • 5.1.1. Basic Properties
  • 5.1.2. Examples of Copulas
  • 5.1.3. Meta Distributions
  • 5.1.4. Simulation of Copulas and Meta Distributions
  • 5.1.5. Further Properties of Copulas
  • 5.1.6. Perfect Dependence
  • 5.2. Dependence Measures
  • 5.2.1. Linear Correlation
  • 5.2.2. Rank Correlation
  • 5.2.3. Coefficients of Tail Dependence
  • 5.3. Normal Mixture Copulas
  • 5.3.1. Tail Dependence
  • 5.3.2. Rank Correlations
  • 5.3.3. Skewed Normal Mixture Copulas
  • 5.3.4. Grouped Normal Mixture Copulas
  • 5.4. Archimedean Copulas
  • 5.4.1. Bivariate Archimedean Copulas
  • 5.4.2. Multivariate Archimedean Copulas
  • 5.4.3. Non-exchangeable Archimedean Copulas
  • 5.5. Fitting Copulas to Data
  • 5.5.1. Method-of-Moments using Rank Correlation
  • 5.5.2. Forming a Pseudo-Sample from the Copula
  • 5.5.3. Maximum Likelihood Estimation
  • 6. Aggregate Risk
  • 6.1. Coherent Measures of Risk
  • 6.1.1. The Axioms of Coherence
  • 6.1.2. Value-at-Risk
  • 6.1.3. Coherent Risk Measures Based on Loss Distributions
  • 6.1.4. Coherent Risk Measures as Generalized Scenarios
  • 6.1.5. Mean-VaR Portfolio Optimization
  • 6.2. Bounds for Aggregate Risks
  • 6.2.1. The General Frechet Problem
  • 6.2.2. The Case of VaR
  • 6.3. Capital Allocation
  • 6.3.1. The Allocation Problem
  • 6.3.2. The Euler Principle and Examples
  • 6.3.3. Economic Justification of the Euler Principle
  • 7. Extreme Value Theory
  • 7.1. Maxima
  • 7.1.1. Generalized Extreme Value Distribution
  • 7.1.2. Maximum Domains of Attraction
  • 7.1.3. Maxima of Strictly Stationary Time Series
  • 7.1.4. The Block Maxima Method
  • 7.2. Threshold Exceedances
  • 7.2.1. Generalized Pareto Distribution
  • 7.2.2. Modelling Excess Losses
  • 7.2.3. Modelling Tails and Measures of Tail Risk
  • 7.2.4. The Hill Method
  • 7.2.5. Simulation Study of EVT Quantile Estimators
  • 7.2.6. Conditional EVT for Financial Time Series
  • 7.3. Tails of Specific Models
  • 7.3.1. Domain of Attraction of Frechet Distribution
  • 7.3.2. Domain of Attraction of Gumbel Distribution
  • 7.3.3. Mixture Models
  • 7.4. Point Process Models
  • 7.4.1. Threshold Exceedances for Strict White Noise
  • 7.4.2. The POT Model
  • 7.4.3. Self-Exciting Processes
  • 7.4.4. A Self-Exciting POT Model
  • 7.5. Multivariate Maxima
  • 7.5.1. Multivariate Extreme Value Copulas
  • 7.5.2. Copulas for Multivariate Minima
  • 7.5.3. Copula Domains of Attraction
  • 7.5.4. Modelling Multivariate Block Maxima
  • 7.6. Multivariate Threshold Exceedances
  • 7.6.1. Threshold Models Using EV Copulas
  • 7.6.2. Fitting a Multivariate Tail Model
  • 7.6.3. Threshold Copulas and Their Limits
  • 8. Credit Risk Management
  • 8.1. Introduction to Credit Risk Modelling
  • 8.1.1. Credit Risk Models
  • 8.1.2. The Nature of the Challenge
  • 8.2. Structural Models of Default
  • 8.2.1. The Merton Model
  • 8.2.2. Pricing in Merton's Model
  • 8.2.3. The KMV Model
  • 8.2.4. Models Based on Credit Migration
  • 8.2.5. Multivariate Firm-Value Models
  • 8.3. Threshold Models
  • 8.3.1. Notation for One-Period Portfolio Models
  • 8.3.2. Threshold Models and Copulas
  • 8.3.3. Industry Examples
  • 8.3.4. Models Based on Alternative Copulas
  • 8.3.5. Model Risk Issues
  • 8.4. The Mixture Model Approach
  • 8.4.1. One-Factor Bernoulli Mixture Models
  • 8.4.2. CreditRisk+
  • 8.4.3. Asymptotics for Large Portfolios
  • 8.4.4. Threshold Models as Mixture Models
  • 8.4.5. Model-Theoretic Aspects of Basel II
  • 8.4.6. Model Risk Issues
  • 8.5. Monte Carlo Methods
  • 8.5.1. Basics of Importance Sampling
  • 8.5.2. Application to Bernoulli-Mixture Models
  • 8.6. Statistical Inference for Mixture Models
  • 8.6.1. Motivation
  • 8.6.2. Exchangeable Bernoulli-Mixture Models
  • 8.6.3. Mixture Models as GLMMs
  • 8.6.4. One-Factor Model with Rating Effect
  • 9. Dynamic Credit Risk Models
  • 9.1. Credit Derivatives
  • 9.1.1. Overview
  • 9.1.2. Single-Name Credit Derivatives
  • 9.1.3. Portfolio Credit Derivatives
  • 9.2. Mathematical Tools
  • 9.2.1. Random Times and Hazard Rates
  • 9.2.2. Modelling Additional Information
  • 9.2.3. Doubly Stochastic Random Times
  • 9.3. Financial and Actuarial Pricing of Credit Risk
  • 9.3.1. Physical and Risk-Neutral Probability Measure
  • 9.3.2. Risk-Neutral Pricing and Market Completeness
  • 9.3.3. Martingale Modelling
  • 9.3.4. The Actuarial Approach to Credit Risk Pricing
  • 9.4. Pricing with Doubly Stochastic Default Times
  • 9.4.1. Recovery Payments of Corporate Bonds
  • 9.4.2. The Model
  • 9.4.3. Pricing Formulas
  • 9.4.4. Applications
  • 9.5. Affine Models
  • 9.5.1. Basic Results
  • 9.5.2. The CIR Square-Root Diffusion
  • 9.5.3. Extensions
  • 9.6. Conditionally Independent Defaults
  • 9.6.1. Reduced-Form Models for Portfolio Credit Risk
  • 9.6.2. Conditionally Independent Default Times
  • 9.6.3. Examples and Applications
  • 9.7. Copula Models
  • 9.7.1. Definition and General Properties
  • 9.7.2. Factor Copula Models
  • 9.8. Default Contagion in Reduced-Form Models
  • 9.8.1. Default Contagion and Default Dependence
  • 9.8.2. Information-Based Default Contagion
  • 9.8.3. Interacting Intensities
  • 10. Operational Risk and Insurance Analytics
  • 10.1. Operational Risk in Perspective
  • 10.1.1. A New Risk Class
  • 10.1.2. The Elementary Approaches
  • 10.1.3. Advanced Measurement Approaches
  • 10.1.4. Operational Loss Data
  • 10.2. Elements of Insurance Analytics
  • 10.2.1. The Case for Acturaial Methodology
  • 10.2.2. The Total Loss Amount
  • 10.2.3. Approximations and Panjer Recursion
  • 10.2.4. Poisson Mixtures
  • 10.2.5. Tails of Aggregate Loss Distributions
  • 10.2.6. The Homogeneous Poisson Process
  • 10.2.7. Processes Related to the Poisson Process
  • Appendix
  • A.1. Miscellaneous Definitions and Results
  • A.1.1. Type of Distribution
  • A.1.2. Generalized Inverses and Quantiles
  • A.1.3. Karamata's Theorem
  • A.2. Probability Distributions
  • A.2.1. Beta
  • A.2.2. Exponential
  • A.2.3. F
  • A.2.4. Gamma
  • A.2.5. Generalized Inverse Gaussian
  • A.2.6. Inverse Gamma
  • A.2.7. Negative Binomial
  • A.2.8. Pareto
  • A.2.9. Stable
  • A.3. Likelihood Inference
  • A.3.1. Maximum Likelihood Estimators
  • A.3.2. Asymptotic Results: Scalar Parameter
  • A.3.3. Asymptotic Results: Vector of Parameters
  • A.3.4. Wald Test and Confidence Intervals
  • A.3.5. Likelihood Ratio Test and Confidence Intervals
  • A.3.6. Akaike Information Criterion
  • References
  • Index
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