Financial Risk Modelling and Portfolio Optimization with R

Preface List of Abbreviations Part One Motivation 1 Introduction References 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 Stylised Facts of Financial Market Returns 3.1.1 Stylised Facts for Univariate Series 3.1.2 Stylised 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 Part Two Risk Modelling 6 Suitable Distributions for Returns 6.1 Preliminaries 6.2 The Generalised Hyperbolic Distribution 6.3 The Generalised Lambda Distribution 6.4 Synopsis of R Packages for GHYP 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 GLDEX 6.5.4 The package gld 6.5.5 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 Stylised 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 The r-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 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 and Kinds 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 Copulae Approaches References Part Three Portfolio Optimisation Approaches 10 Robust Portfolio Optimisation 10.1 Overview 10.2 Robust Statistics 10.2.1 Motivation 10.2.2 Selected Robust Estimators 10.3 Robust Optimisation 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 Application 10.5.1 Portfolio Simulation: Robust vs. Classical Statistics 10.5.2 Portfolio Back Test: Robust vs. Classical Statistics 10.5.3 Portfolio Back Test: Robust Optimisation 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 R 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 Backtest 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 Optimisation 13.6.2 Copula Opinion Pooling 13.6.3 Protection Strategies References A Package Overview A.1 Packages in Alphabetical Order A.2 Packages Ordered by Topic B Time Series Data B.1 Date-Time Classes B.2 The ts-Class in the base package stats B.3 Irregular-Spaced TimeSeries B.4 The package timeSeries B.5 The package zoo B.6 The packages tframe and xts 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 D Technicalities References Index