Beschreibung

Volume 4: Value at Risk ModelsAs a set these books are a radical update and revision of Market Models: a Guide to Financial Data Analysis. They provide a rigorous explanation of the key theoretical ideas that market model developers are faced with, in practical, clear terms. Developers are faced with many decisions, about the pricing, the data, the statistical methodology and the calibration and testing of the model prior to implementation therefore these books help risk managers, quantitative traders and investment analysts make the right decisions. The emphasis throughout is in understanding concepts and implementing solutions, assisted by the use of real-world examples both in the text and, interactively, on the accompanying CDs. All of the CD's contain VBA code&C++ code, plus VBA with Excel interfaceValue at Risk is a radical update and revision of the VaR aspects covered in the highly successful Market Models: A Guide to Financial Data Analysis creating a new, stand-alone book forming Part IV of the four volume set on market models. Iit contains significant new material including Model Risk, Economic Capital and Risk Capital - all important new areas in risk management. The book takes the reader through the basic concepts of VaR right through to the most advanced, practical solutions that they will need to develop models, covering; Analytic VaR for Linear Portfolio, Historical Simulation Methods, Monte Carlo Simulation Methods, VaR for Options Portfolios, VaR Model Risk, Stress Testing Portfolios and how to calculate Risk CapitalAll material is supported by Excel spreadsheets based on real-world examples, which will also be included on the accompanying CD so that the reader is provided with complete solutions.

Inhaltsangabe

List of Figures.

List of Tables.

List of Examples.

Foreword.

Preface to Volume IV.

IV. 1 Value at Risk and Other Risk Metrics.

IV. 1.1 Introduction.

IV. 1.2 An Overview of Market Risk Assessment.

IV. 1.3 Downside and Quantile Risk Metrics.

IV. 1.4 Defining Value at Risk.

IV. 1.5 Foundations of Value-at-Risk Measurement.

IV. 1.6 Risk Factor Value at Risk.

IV. 1.7 Decomposition of Value at Risk.

IV. 1.8 Risk Metrics Associated with Value at Risk.

IV. 1.9 Introduction to Value-at-Risk Models.

IV. 1.10 Summary and Conclusions.

IV. 2 Parametric Linear VaR Models.

IV. 2.1 Introduction.

IV. 2.2 Foundations of Normal Linear Value at Risk.

IV. 2.3 Normal Linear Value at Risk for Cash-Flow Maps.

IV. 2.4 Case Study: PC Value at Risk of a UK Fixed Income Portfolio.

IV. 2.5 Normal Linear Value at Risk for Stock Portfolios.

IV. 2.6 Systematic Value-at-Risk Decomposition for Stock Portfolios.

IV. 2.7 Case Study: Normal Linear Value at Risk for Commodity Futures.

IV. 2.8 Student t Distributed Linear Value at Risk.

IV. 2.9 Linear Value at Risk with Mixture Distributions.

IV. 2.10 Exponential Weighting with Parametric Linear Value at Risk.

IV. 2.11 Expected Tail Loss (Conditional VaR).


IV. 2.12 Case Study: Credit Spread Parametric Linear Value at Risk and ETL.

IV. 2.13 Summary and Conclusions.

IV. 3 Historical Simulation.

IV. 3.1 Introduction.

IV. 3.2 Properties of Historical Value at Risk.

IV. 3.3 Improving the Accuracy of Historical Value at Risk.

IV. 3.4 Precision of Historical Value at Risk at Extreme Quantiles.

IV. 3.5 Historical Value at Risk for Linear Portfolios.

IV. 3.6 Estimating Expected Tail Loss in the Historical Value-at-Risk Model.

IV. 3.7 Summary and Conclusions.

IV. 4 Monte Carlo VaR.

IV. 4.1 Introduction.

IV. 4.2 Basic Concepts.

IV. 4.3 Modelling Dynamic Properties in Risk Factor Returns.

IV. 4.4 Modelling Risk Factor Dependence.

IV. 4.5 Monte Carlo Value at Risk for Linear Portfolios.

IV. 4.6 Summary and Conclusions.

IV. 5 Value at Risk for Option Portfolios.

IV. 5.1 Introduction.

IV. 5.2 Risk Characteristics of Option Portfolios.

IV. 5.3 Analytic Value-at-Risk Approximations.

IV. 5.4 Historical Value at Risk for Option Portfolios.

IV. 5.5 Monte Carlo Value at Risk for Option Portfolios.

IV. 5.6 Summary and Conclusions.

IV. 6 Risk Model Risk.

IV. 6.1 Introduction.

IV. 6.2 Sources of Risk Model Risk.

IV. 6.3 Estimation Risk.

IV. 6.4 Model Validation.

IV. 6.5 Summary and Conclusions.

IV. 7 Scenario Analysis and Stress Testing.

IV. 7.1 Introduction.

IV. 7.2 Scenarios on Financial Risk Factors.

IV. 7.3 Scenario Value at Risk and Expected Tail Loss.

IV. 7.4 Introduction to Stress Testing.

IV. 7.5 A Coherent Framework for Stress Testing.

IV. 7.6 Summary and Conclusions.

IV. 8 Capital Allocation.

IV. 8.1 Introduction.

IV. 8.2 Minimum Market Risk Capital Requirements for Banks.

IV. 8.3 Economic Capital Allocation.

IV. 8.4 Summary and Conclusions.

References.

Index.

Klappentext

Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice.

All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include:
* Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL);
* New formulae for VaR based on autocorrelated returns;
* Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR;
* Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas;
* Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios;
* Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components;
* Backtesting and the assessment of risk model risk;
* Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL.

Sonstiges

Lieferung vom Verlag mit leichten Qualitätsmängeln möglich