The measurement of market risk : modelling of risk factors, asset pricing, and approximation of portfolio distributions /

The measurement of market risk : modelling of risk factors, asset pricing, and approximation of portfolio distributions /

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Bibliographic Details
Author / Creator:Moix, Pierre-Yves, 1965-
Imprint:New York : Springer, 2001.
Description:xi, 272 p. : 36 fig., 37 tab.
Language:English
Series:Lecture notes in economics and mathematical systems ; 504
Lecture notes in economics and mathematical systems 504.
Subject:
Financial futures -- Mathematical models.
Risk management -- Mathematical models.
Options (Finance) -- Prices -- Mathematical models.
Capital assets pricing model.
Portfolio management -- Mathematical models.
Capital assets pricing model.
Financial futures -- Mathematical models.
Options (Finance) -- Prices -- Mathematical models.
Portfolio management -- Mathematical models.
Risk management -- Mathematical models.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4471257
Hidden Bibliographic Details
ISBN:3540421432 (pbk. : alk. paper)
Notes:Rev. of the author's thesis (doctoral)--University of St. Gallen, 1999.
Includes bibliographical references and index.
Table of Contents:
  • 1. Introduction
  • 1.1. The Need for Risk Measurement
  • 1.2. The Nature of Financial Risk
  • 1.3. Formal Framework
  • 1.3.1. Modelling the Uncertainty
  • 1.3.2. The Information Structure
  • 1.4. Problem Statement
  • 1.5. Structure of the Book
  • 1.6. Test Environment
  • 1.6.1. Environment I
  • 1.6.2. Environment II
  • 2. Risk and Risk Measures
  • 2.1. The Investment Decision
  • 2.1.1. Utility Theory and Expected Utility Hypothesis
  • 2.1.2. Rules for the Ordering of Uncertain Prospects
  • 2.2. The Capital Requirement Decision
  • 2.2.1. Value-at-Risk
  • 2.2.2. Coherent Risk Measures
  • 2.3. Summary
  • 3. Modelling the Dynamics of the Risk Factors
  • 3.1. Statistical Definitions
  • 3.1.1. Stochastic Processes: Basic Definitions
  • 3.1.2. Properties of Stochastic Processes
  • 3.1.3. Basic Stochastic Processes
  • 3.2. The Economic Assumption: the Efficient Market Hypothesis
  • 3.3. Empirical Evidence for the Returns
  • 3.3.1. Calendar Effects
  • 3.3.2. Leptokurtosis and Weak Evidence of Skewness
  • 3.3.3. The Autocorrelation of the Squared Returns
  • 3.4. Models for the Risk Factor Dynamics
  • 3.4.1. The Generic Model for the Log-returns
  • 3.4.2. ARCH Models
  • 3.4.3. Stochastic Variance Models
  • 3.5. Empirical Analysis of the Returns on Swiss Stocks
  • 3.5.1. The Data
  • 3.5.2. Descriptive Statistics and Correlation
  • 3.5.3. Implementation of an Alternative Model
  • 3.5.4. Impact of the Alternative Modelling
  • 3.6. Continuous-Time Models
  • 3.7. Summary
  • 4. Valuation of Financial Instruments
  • 4.1. Principles of Valuation
  • 4.1.1. Valuation by Arbitrage
  • 4.2. Cash Instruments
  • 4.2.1. Equities
  • 4.2.2. Fixed-Income Instruments
  • 4.3. Futures and Forwards
  • 4.4. Options
  • 4.4.1. The Black-Scholes Analysis
  • 4.4.2. Risk-Neutral Valuation
  • 4.4.3. Numerical Approaches
  • 4.5. Approximation of the Value Function
  • 4.5.1. Global Taylor Approximation for Option Pricing
  • 4.5.2. Piecewise Taylor Approximations
  • 5. Approximation of the Portfolio Distribution
  • 5.1. Analytical Methods
  • 5.1.1. Delta Approximation
  • 5.1.2. Delta-Gamma Approximation
  • 5.2. Generation of Scenarios
  • 5.2.1. The Pseudo-Random Method
  • 5.2.2. The Quasi-Random Method
  • 5.2.3. Generation of Distributions for the Risk Factors
  • 5.3. Monte Carlo Simulation
  • 5.3.1. Error Analysis
  • 5.3.2. Variance Reduction Techniques
  • 5.4. The BDPQA
  • 5.4.1. Simplices
  • 5.4.2. Simplicial Coverage of the Risk Factor Distribution
  • 5.4.3. Barycentric Discretisation
  • 5.4.4. Approximation of the Portfolio Distribution
  • 5.4.5. Reinement Strategies
  • 5.4.6. Numerical Example
  • 5.5. Benchmarking the BDPQA
  • 5.5.1. The Choice of the Holding Period
  • 5.6. Summary
  • 6. Sample Estimation of Risk Measures
  • 6.1. Introduction
  • 6.2. Order Statistics
  • 6.2.1. Distribution of Order Statistics
  • 6.2.2. Moments of Order Statistics
  • 6.2.3. Conidence Interval for Population Quantiles
  • 6.3. Quantile Estimators Based on Order Statistics
  • 6.3.1. Linear Combination of Several Order Statistics
  • 6.4. Kernel-Based Estimators
  • 6.4.1. Accuracy of the Estimate Density
  • 6.4.2. Bandwidth Selection
  • 6.4.3. Quantile Estimation Based on the Kernel Density Method
  • 6.5. Comparison of the Quantile Estimators
  • 6.6. Summary
  • 7. Conclusion and Outlook
  • 7.1. Summary
  • 7.2. The Issue of Credit Risk
  • 7.3. Outlook
  • A. Probability and Statistics
  • A.l. Probabilistic Modelling
  • A.2. Random Variable
  • A.2.1. Distribution Function
  • A.2.2. Moments
  • A.2.3. Independence and Correlation
  • A.2.4. Conditional Probability and Expectation
  • A.2.5. Stochastic Processes and Information Structure
  • A.2.6. Martingales
  • A.3. Selected Distributions
  • A.3.1. Basic Distributions
  • A.3.2. Elliptically Contoured Distibutions
  • A.3.3. Stable Distribution
  • A.4. Types of Convergence
  • A.5. Sampling Theory
  • Bibliography
  • List of Figures
  • List of Tables
  • Index

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