Quantitative Risk Management

Höfundar: Alexander J. McNeil; RĂ¼diger Frey; Paul Embrechts (Útgáfa: 0)
Quantitative Risk Management

Kaup valmöguleikar

Þessi bók veitir ítarlega umfjöllun um fræðileg hugtök og líkanagerð í megindlegri áhættustýringu. Hvort sem þú starfar sem sérfræðingur í fjármálaáhættu, tryggingastærðfræðingur eða eftirlitsaðili eða stundar nám í megindlegri fjármálafræði veitir Quantitative Risk Management þér hagnýt verkfæri til að leysa raunveruleg viðfangsefni. Fjallað er um nýjustu framfarir á sviðinu og aðferðir við líkanagerð markaðsáhættu, útlánaáhættu og rekstraráhættu.

Hefðbundnar aðferðir atvinnulífsins eru settar í formlegra fræðilegt samhengi og lykilhugtök á borð við tapdreifingar, áhættumælikvarða og meginreglur um samþættingu og úthlutun áhættu eru skoðuð. Aðferðafræði bókarinnar byggist á fjölbreyttum megindlegum fræðigreinum, allt frá stærðfræðilegri fjármálafræði og tölfræði til hagrannsókna og tryggingastærðfræði. Rauður þráður bókarinnar er nauðsyn þess að taka með fullnægjandi hætti tillit til öfgakenndra niðurstaðna og innbyrðis tengsla helstu áhættuþátta.

Bókin hefur sannað gildi sitt í kennslu og fjallar einnig um ítarlegri viðfangsefni á borð við útlánaafleiður. Hún hefur verið endurskoðuð og aukin til að endurspegla þróunina á sviðinu frá fjármálakreppunni.

  • Styttri kaflar auðvelda kennslu og nám.
  • Ítarlegri umfjöllun um Solvency II og áhættustýringu í vátryggingum.
  • Aukin umfjöllun um útlánaáhættu, þar á meðal mótaðilaáhættu og verðlagningu CDO.
  • Nýr kafli um markaðsáhættu og nýtt efni um áhættumælikvarða og samþættingu áhættu.

Nánar um bókina

Útgefandi
Princeton University Press
ISBN
9781400866281
Print ISBN
9780691166278
Format
Page Fidelity (PDF)
Útgáfa
0
Höfundar
Alexander J. McNeil; RĂ¼diger Frey; Paul Embrechts
Tungumál
English
Útgefið
2015-05-26
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Title
  • Copyright
  • Dedication
  • Contents
  • Preface
  • I An Introduction to Quantitative Risk Management
  • 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 Regulatory Framework
  • 1.3.1 The Basel Framework
  • 1.3.2 The Solvency II Framework
  • 1.3.3 Criticism of Regulatory Frameworks
  • 1.4 Why Manage Financial Risk?
  • 1.4.1 A Societal View
  • 1.4.2 The Shareholder’s View
  • 1.5 Quantitative Risk Management
  • 1.5.1 The Q in QRM
  • 1.5.2 The Nature of the Challenge
  • 1.5.3 QRM Beyond Finance
  • 2 Basic Concepts in Risk Management
  • 2.1 Risk Management for a Financial Firm
  • 2.1.1 Assets, Liabilities and the Balance Sheet
  • 2.1.2 Risks Faced by a Financial Firm
  • 2.1.3 Capital
  • 2.2 Modelling Value and Value Change
  • 2.2.1 Mapping Risks
  • 2.2.2 Valuation Methods
  • 2.2.3 Loss Distributions
  • 2.3 Risk Measurement
  • 2.3.1 Approaches to Risk Measurement
  • 2.3.2 Value-at-Risk
  • 2.3.3 VaR in Risk Capital Calculations
  • 2.3.4 Other Risk Measures Based on Loss Distributions
  • 2.3.5 Coherent and Convex Risk Measures
  • 3 Empirical Properties of Financial Data
  • 3.1 Stylized Facts of Financial Return Series
  • 3.1.1 Volatility Clustering
  • 3.1.2 Non-normality and Heavy Tails
  • 3.1.3 Longer-Interval Return Series
  • 3.2 Multivariate Stylized Facts
  • 3.2.1 Correlation between Series
  • 3.2.2 Tail Dependence
  • II Methodology
  • 4 Financial Time Series
  • 4.1 Fundamentals of Time Series Analysis
  • 4.1.1 Basic Definitions
  • 4.1.2 ARMA Processes
  • 4.1.3 Analysis in the Time Domain
  • 4.1.4 Statistical Analysis of Time Series
  • 4.1.5 Prediction
  • 4.2 GARCH Models for Changing Volatility
  • 4.2.1 ARCH Processes
  • 4.2.2 GARCH Processes
  • 4.2.3 Simple Extensions of the GARCH Model
  • 4.2.4 Fitting GARCH Models to Data
  • 4.2.5 Volatility Forecasting and Risk Measure Estimation
  • 5 Extreme Value Theory
  • 5.1 Maxima
  • 5.1.1 Generalized Extreme Value Distribution
  • 5.1.2 Maximum Domains of Attraction
  • 5.1.3 Maxima of Strictly Stationary Time Series
  • 5.1.4 The Block Maxima Method
  • 5.2 Threshold Exceedances
  • 5.2.1 Generalized Pareto Distribution
  • 5.2.2 Modelling Excess Losses
  • 5.2.3 Modelling Tails and Measures of Tail Risk
  • 5.2.4 The Hill Method
  • 5.2.5 Simulation Study of EVT Quantile Estimators
  • 5.2.6 Conditional EVT for Financial Time Series
  • 5.3 Point Process Models
  • 5.3.1 Threshold Exceedances for Strict White Noise
  • 5.3.2 The POT Model
  • 6 Multivariate Models
  • 6.1 Basics of Multivariate Modelling
  • 6.1.1 Random Vectors and Their Distributions
  • 6.1.2 Standard Estimators of Covariance and Correlation
  • 6.1.3 The Multivariate Normal Distribution
  • 6.1.4 Testing Multivariate Normality
  • 6.2 Normal Mixture Distributions
  • 6.2.1 Normal Variance Mixtures
  • 6.2.2 Normal Mean–Variance Mixtures
  • 6.2.3 Generalized Hyperbolic Distributions
  • 6.2.4 Empirical Examples
  • 6.3 Spherical and Elliptical Distributions
  • 6.3.1 Spherical Distributions
  • 6.3.2 Elliptical Distributions
  • 6.3.3 Properties of Elliptical Distributions
  • 6.3.4 Estimating Dispersion and Correlation
  • 6.4 Dimension-Reduction Techniques
  • 6.4.1 Factor Models
  • 6.4.2 Statistical Estimation Strategies
  • 6.4.3 Estimating Macroeconomic Factor Models
  • 6.4.4 Estimating Fundamental Factor Models
  • 6.4.5 Principal Component Analysis
  • 7 Copulas and Dependence
  • 7.1 Copulas
  • 7.1.1 Basic Properties
  • 7.1.2 Examples of Copulas
  • 7.1.3 Meta Distributions
  • 7.1.4 Simulation of Copulas and Meta Distributions
  • 7.1.5 Further Properties of Copulas
  • 7.2 Dependence Concepts and Measures
  • 7.2.1 Perfect Dependence
  • 7.2.2 Linear Correlation
  • 7.2.3 Rank Correlation
  • 7.2.4 Coefficients of Tail Dependence
  • 7.3 Normal Mixture Copulas
  • 7.3.1 Tail Dependence
  • 7.3.2 Rank Correlations
  • 7.3.3 Skewed Normal Mixture Copulas
  • 7.3.4 Grouped Normal Mixture Copulas
  • 7.4 Archimedean Copulas
  • 7.4.1 Bivariate Archimedean Copulas
  • 7.4.2 Multivariate Archimedean Copulas
  • 7.5 Fitting Copulas to Data
  • 7.5.1 Method-of-Moments Using Rank Correlation
  • 7.5.2 Forming a Pseudo-sample from the Copula
  • 7.5.3 Maximum Likelihood Estimation
  • 8 Aggregate Risk
  • 8.1 Coherent and Convex Risk Measures
  • 8.1.1 Risk Measures and Acceptance Sets
  • 8.1.2 Dual Representation of Convex Measures of Risk
  • 8.1.3 Examples of Dual Representations
  • 8.2 Law-Invariant Coherent Risk Measures
  • 8.2.1 Distortion Risk Measures
  • 8.2.2 The Expectile Risk Measure
  • 8.3 Risk Measures for Linear Portfolios
  • 8.3.1 Coherent Risk Measures as Stress Tests
  • 8.3.2 Elliptically Distributed Risk Factors
  • 8.3.3 Other Risk Factor Distributions
  • 8.4 Risk Aggregation
  • 8.4.1 Aggregation Based on Loss Distributions
  • 8.4.2 Aggregation Based on Stressing Risk Factors
  • 8.4.3 Modular versus Fully Integrated Aggregation Approaches
  • 8.4.4 Risk Aggregation and Fréchet Problems
  • 8.5 Capital Allocation
  • 8.5.1 The Allocation Problem
  • 8.5.2 The Euler Principle and Examples
  • 8.5.3 Economic Properties of the Euler Principle
  • III Applications
  • 9 Market Risk
  • 9.1 Risk Factors and Mapping
  • 9.1.1 The Loss Operator
  • 9.1.2 Delta and Delta–Gamma Approximations
  • 9.1.3 Mapping Bond Portfolios
  • 9.1.4 Factor Models for Bond Portfolios
  • 9.2 Market Risk Measurement
  • 9.2.1 Conditional and Unconditional Loss Distributions
  • 9.2.2 Variance–Covariance Method
  • 9.2.3 Historical Simulation
  • 9.2.4 Dynamic Historical Simulation
  • 9.2.5 Monte Carlo
  • 9.2.6 Estimating Risk Measures
  • 9.2.7 Losses over Several Periods and Scaling
  • 9.3 Backtesting
  • 9.3.1 Violation-Based Tests for VaR
  • 9.3.2 Violation-Based Tests for Expected Shortfall
  • 9.3.3 Elicitability and Comparison of Risk Measure Estimates
  • 9.3.4 Empirical Comparison of Methods Using Backtesting Concepts
  • 9.3.5 Backtesting the Predictive Distribution
  • 10 Credit Risk
  • 10.1 Credit-Risky Instruments
  • 10.1.1 Loans
  • 10.1.2 Bonds
  • 10.1.3 Derivative Contracts Subject to Counterparty Risk
  • 10.1.4 Credit Default Swaps and Related Credit Derivatives
  • 10.1.5 PD, LGD and EAD
  • 10.2 Measuring Credit Quality
  • 10.2.1 Credit Rating Migration
  • 10.2.2 Rating Transitions as a Markov Chain
  • 10.3 Structural Models of Default
  • 10.3.1 The Merton Model
  • 10.3.2 Pricing in Merton’s Model
  • 10.3.3 Structural Models in Practice: EDF and DD
  • 10.3.4 Credit-Migration Models Revisited
  • 10.4 Bond and CDS Pricing in Hazard Rate Models
  • 10.4.1 Hazard Rate Models
  • 10.4.2 Risk-Neutral Pricing Revisited
  • 10.4.3 Bond Pricing
  • 10.4.4 CDS Pricing
  • 10.4.5 P versus Q: Empirical Results
  • 10.5 Pricing with Stochastic Hazard Rates
  • 10.5.1 Doubly Stochastic Random Times
  • 10.5.2 Pricing Formulas
  • 10.5.3 Applications
  • 10.6 Affine Models
  • 10.6.1 Basic Results
  • 10.6.2 The CIR Square-Root Diffusion
  • 10.6.3 Extensions
  • 11 Portfolio Credit Risk Management
  • 11.1 Threshold Models
  • 11.1.1 Notation for One-Period Portfolio Models
  • 11.1.2 Threshold Models and Copulas
  • 11.1.3 Gaussian Threshold Models
  • 11.1.4 Models Based on Alternative Copulas
  • 11.1.5 Model Risk Issues
  • 11.2 Mixture Models
  • 11.2.1 Bernoulli Mixture Models
  • 11.2.2 One-Factor Bernoulli Mixture Models
  • 11.2.3 Recovery Risk in Mixture Models
  • 11.2.4 Threshold Models as Mixture Models
  • 11.2.5 Poisson Mixture Models and CreditRisk^+
  • 11.3 Asymptotics for Large Portfolios
  • 11.3.1 Exchangeable Models
  • 11.3.2 General Results
  • 11.3.3 The Basel IRB Formula
  • 11.4 Monte Carlo Methods
  • 11.4.1 Basics of Importance Sampling
  • 11.4.2 Application to Bernoulli Mixture Models
  • 11.5 Statistical Inference in Portfolio Credit Models
  • 11.5.1 Factor Modelling in Industry Threshold Models
  • 11.5.2 Estimation of Bernoulli Mixture Models
  • 11.5.3 Mixture Models as GLMMs
  • 11.5.4 A One-Factor Model with Rating Effect
  • 12 Portfolio Credit Derivatives
  • 12.1 Credit Portfolio Products
  • 12.1.1 Collateralized Debt Obligations
  • 12.1.2 Credit Indices and Index Derivatives
  • 12.1.3 Basic Pricing Relationships for Index Swaps and CDOs
  • 12.2 Copula Models
  • 12.2.1 Definition and Properties
  • 12.2.2 Examples
  • 12.3 Pricing of Index Derivatives in Factor Copula Models
  • 12.3.1 Analytics
  • 12.3.2 Correlation Skews
  • 12.3.3 The Implied Copula Approach
  • 13 Operational Risk and Insurance Analytics
  • 13.1 Operational Risk in Perspective
  • 13.1.1 An Important Risk Class
  • 13.1.2 The Elementary Approaches
  • 13.1.3 Advanced Measurement Approaches
  • 13.1.4 Operational Loss Data
  • 13.2 Elements of Insurance Analytics
  • 13.2.1 The Case for Actuarial Methodology
  • 13.2.2 The Total Loss Amount
  • 13.2.3 Approximations and Panjer Recursion
  • 13.2.4 Poisson Mixtures
  • 13.2.5 Tails of Aggregate Loss Distributions
  • 13.2.6 The Homogeneous Poisson Process
  • 13.2.7 Processes Related to the Poisson Process
  • IV Special Topics
  • 14 Multivariate Time Series
  • 14.1 Fundamentals of Multivariate Time Series
  • 14.1.1 Basic Definitions
  • 14.1.2 Analysis in the Time Domain
  • 14.1.3 Multivariate ARMA Processes
  • 14.2 Multivariate GARCH Processes
  • 14.2.1 General Structure of Models
  • 14.2.2 Models for Conditional Correlation
  • 14.2.3 Models for Conditional Covariance
  • 14.2.4 Fitting Multivariate GARCH Models
  • 14.2.5 Dimension Reduction in MGARCH
  • 14.2.6 MGARCH and Conditional Risk Measurement
  • 15 Advanced Topics in Multivariate Modelling
  • 15.1 Normal Mixture and Elliptical Distributions
  • 15.1.1 Estimation of Generalized Hyperbolic Distributions
  • 15.1.2 Testing for Elliptical Symmetry
  • 15.2 Advanced Archimedean Copula Models
  • 15.2.1 Characterization of Archimedean Copulas
  • 15.2.2 Non-exchangeable Archimedean Copulas
  • 16 Advanced Topics in Extreme Value Theory
  • 16.1 Tails of Specific Models
  • 16.1.1 Domain of Attraction of the Fréchet Distribution
  • 16.1.2 Domain of Attraction of the Gumbel Distribution
  • 16.1.3 Mixture Models
  • 16.2 Self-exciting Models for Extremes
  • 16.2.1 Self-exciting Processes
  • 16.2.2 A Self-exciting POT Model
  • 16.3 Multivariate Maxima
  • 16.3.1 Multivariate Extreme Value Copulas
  • 16.3.2 Copulas for Multivariate Minima
  • 16.3.3 Copula Domains of Attraction
  • 16.3.4 Modelling Multivariate Block Maxima
  • 16.4 Multivariate Threshold Exceedances
  • 16.4.1 Threshold Models Using EV Copulas
  • 16.4.2 Fitting a Multivariate Tail Model
  • 16.4.3 Threshold Copulas and Their Limits
  • 17 Dynamic Portfolio Credit Risk Models and Counterparty Risk
  • 17.1 Dynamic Portfolio Credit Risk Models
  • 17.1.1 Why Dynamic Models of Portfolio Credit Risk?
  • 17.1.2 Classes of Reduced-Form Models of Portfolio Credit Risk
  • 17.2 Counterparty Credit Risk Management
  • 17.2.1 Uncollateralized Value Adjustments for a CDS
  • 17.2.2 Collateralized Value Adjustments for a CDS
  • 17.3 Conditionally Independent Default Times
  • 17.3.1 Definition and Mathematical Properties
  • 17.3.2 Examples and Applications
  • 17.3.3 Credit Value Adjustments
  • 17.4 Credit Risk Models with Incomplete Information
  • 17.4.1 Credit Risk and Incomplete Information
  • 17.4.2 Pure Default Information
  • 17.4.3 Additional Information
  • 17.4.4 Collateralized Credit Value Adjustments and Contagion Effects
  • Appendix
  • A.1 Miscellaneous Definitions and Results
  • A.1.1 Type of Distribution
  • A.1.2 Generalized Inverses and Quantiles
  • A.1.3 Distributional Transform
  • A.1.4 Karamata’s Theorem
  • A.1.5 Supporting and Separating Hyperplane Theorems
  • 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