Financial risk modelling and portfolio optimization with R /

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Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. Financial Risk Modelling and Portfolio Optimisation with R: Demonstrates...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pfaff, Bernhard
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, West Sussex, UK : John Wiley & Sons, 2013.
Colección:Statistics in practice.
Temas:
Financial risk > Mathematical models.
Portfolio management.
R (Computer program language)
Risque financier > Modèles mathématiques.
Gestion de portefeuille.
R (Langage de programmation)
BUSINESS & ECONOMICS > Finance.
Portfolio management
Portföljförvaltning.
R (programspråk)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: pt. I MOTIVATION
  • 1. Introduction
  • Reference
  • 2. A brief course in R
  • 2.1. Origin and development
  • 2.2. Getting help
  • 2.3. Working with R
  • 2.4. Classes, methods and functions
  • 2.5. The accompanying package FRAPO
  • References
  • 3. Financial market data
  • 3.1. Stylized facts on financial market returns
  • 3.1.1. Stylized facts for univariate series
  • 3.1.2. Stylized facts for multivariate series
  • 3.2. Implications for risk models
  • References
  • 4. Measuring risks
  • 4.1. Introduction
  • 4.2. Synopsis of risk measures
  • 4.3. Portfolio risk concepts
  • References
  • 5. Modern portfolio theory
  • 5.1. Introduction
  • 5.2. Markowitz portfolios
  • 5.3. Empirical mean-variance portfolios
  • References
  • pt. II RISK MODELLING
  • 6. Suitable distributions for returns
  • 6.1. Preliminaries
  • 6.2. The generalized hyperbolic distribution
  • 6.3. The generalized lambda distribution
  • 6.4. Synopsis of R packages for the GHD
  • 6.4.1. The package fBasics
  • 6.4.2. The package GeneralizedHyperbolic
  • 6.4.3. The package ghyp
  • 6.4.4. The package QRM
  • 6.4.5. The package SkewHyperbolic
  • 6.4.6. The package VarianceGamma
  • 6.5. Synopsis of R packages for GLD
  • 6.5.1. The package Davies
  • 6.5.2. The package fBasics
  • 6.5.3. The package gld
  • 6.5.4. The package lmomco
  • 6.6. Applications of the GHD to risk modelling
  • 6.6.1. Fitting stock returns to the GHD
  • 6.6.2. Risk assessment with the GHD
  • 6.6.3. Stylized facts revisited
  • 6.7. Applications of the GLD to risk modelling and data analysis
  • 6.7.1. VaR for a single stock
  • 6.7.2. Shape triangle for FTSE 100 constituents
  • References
  • 7. Extreme value theory
  • 7.1. Preliminaries
  • 7.2. Extreme value methods and models
  • 7.2.1. The block maxima approach
  • 7.2.2. rth largest order models
  • 7.2.3. The peaks-over-threshold approach
  • 7.3. Synopsis of R packages
  • 7.3.1. The package evd
  • 7.3.2. The package evdbayes
  • 7.3.3. The package evir
  • 7.3.4. The package fExtremes
  • 7.3.5. The packages ismev and extRemes
  • 7.3.6. The package POT
  • 7.3.7. The package QRM
  • 7.3.8. The package Renext
  • 7.4. Empirical applications of EVT
  • 7.4.1. Section outline
  • 7.4.2. Block maxima model for Siemens
  • 7.4.3. r block maxima model for BMW
  • 7.4.4. POT method for Boeing
  • References
  • 8. Modelling volatility
  • 8.1. Preliminaries
  • 8.2. The class of ARCH models
  • 8.3. Synopsis of R packages
  • 8.3.1. The package bayesGARCH
  • 8.3.2. The package ccgarch
  • 8.3.3. The package fGarch
  • 8.3.4. The package gogarch
  • 8.3.5. The packages rugarch and rmgarch
  • 8.3.6. The package tseries
  • 8.4. Empirical application of volatility models
  • References
  • 9. Modelling dependence
  • 9.1. Overview
  • 9.2. Correlation, dependence and distributions
  • 9.3. Copulae
  • 9.3.1. Motivation
  • 9.3.2. Correlations and dependence revisited
  • 9.3.3. Classification of copulae
  • 9.4. Synopsis of R packages
  • 9.4.1. The package BLCOP
  • 9.4.2. The packages copula and nacopula
  • 9.4.3. The package fCopulae
  • 9.4.4. The package gumbel
  • 9.4.5. The package QRM
  • 9.5. Empirical applications of copulae
  • 9.5.1. GARCH-copula model
  • 9.5.2. Mixed copula approaches
  • References
  • pt. III PORTFOLIO OPTIMIZATION APPROACHES
  • 10. Robust portfolio optimization
  • 10.1. Overview
  • 10.2. Robust statistics
  • 10.2.1. Motivation
  • 10.2.2. Selected robust estimators
  • 10.3. Robust optimization
  • 10.3.1. Motivation
  • 10.3.2. Uncertainty sets and problem formulation
  • 10.4. Synopsis of R packages
  • 10.4.1. The package covRobust
  • 10.4.2. The package fPortfolio
  • 10.4.3. The package MASS
  • 10.4.4. The package robustbase
  • 10.4.5. The package robust
  • 10.4.6. The package rrcov
  • 10.4.7. The package Rsocp
  • 10.5. Empirical applications
  • 10.5.1. Portfolio simulation: Robust versus classical statistics
  • 10.5.2. Portfolio back-test: Robust versus classical statistics
  • 10.5.3. Portfolio back-test: Robust optimization
  • References
  • 11. Diversification reconsidered
  • 11.1. Introduction
  • 11.2. Most diversified portfolio
  • 11.3. Risk contribution constrained portfolios
  • 11.4. Optimal tail-dependent portfolios
  • 11.5. Synopsis of R packages
  • 11.5.1. The packages DEoptim and RcppDE
  • 11.5.2. The package FRAPO
  • 11.5.3. The package PortfolioAnalytics
  • 11.6. Empirical applications
  • 11.6.1. Comparison of approaches
  • 11.6.2. Optimal tail-dependent portfolio against benchmark
  • 11.6.3. Limiting contributions to expected shortfall
  • References
  • 12. Risk-optimal portfolios
  • 12.1. Overview
  • 12.2. Mean-VaR portfolios
  • 12.3. Optimal CVaR portfolios
  • 12.4. Optimal draw-down portfolios
  • 12.5. Synopsis of R packages
  • 12.5.1. The package fPortfolio
  • 12.5.2. The package FRAPO
  • 12.5.3. Packages for linear programming
  • 12.5.4. The package PerformanceAnalytics
  • 12.6. Empirical applications
  • 12.6.1. Minimum-CVaR versus minimum-variance portfolios
  • 12.6.2. Draw-down constrained portfolios
  • 12.6.3. Back-test comparison for stock portfolio
  • References
  • 13. Tactical asset allocation
  • 13.1. Overview
  • 13.2. Survey of selected time series models
  • 13.2.1. Univariate time series models
  • 13.2.2. Multivariate time series models
  • 13.3. Black-Litterman approach
  • 13.4. Copula opinion and entropy pooling
  • 13.4.1. Introduction
  • 13.4.2. The COP model
  • 13.4.3. The EP model
  • 13.5. Synopsis of R packages
  • 13.5.1. The package BLCOP
  • 13.5.2. The package dse
  • 13.5.3. The package fArma
  • 13.5.4. The package forecast
  • 13.5.5. The package MSBVAR
  • 13.5.6. The package PairTrading
  • 13.5.7. The packages urca and vars
  • 13.6. Empirical applications
  • 13.6.1. Black-Litterman portfolio optimization
  • 13.6.2. Copula opinion pooling
  • 13.6.3. Protection strategies
  • References
  • Appendix A Package overview
  • A.1. Packages in alphabetical order
  • A.2. Packages ordered by topic
  • References
  • Appendix B Time series data
  • B.1. Date-time classes
  • B.2. The ts class in the base package stats
  • B.3. Irregular-spaced time series
  • B.4. The package timeSeries
  • B.5. The package zoo
  • B.6. The packages tframe and xts
  • References
  • Appendix C Back-testing and reporting of portfolio strategies
  • C.1. R packages for back-testing
  • C.2. R facilities for reporting
  • C.3. Interfacing databases
  • References
  • Appendix D Technicalities.

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