Cover

Title Page

Copyright

Contents

Preface to the Second Edition

Preface

Abbreviations

About the Companion Website

Part I Motivation

Chapter 1 Introduction

Reference

Chapter 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

Chapter 3 Financial market data

3.1 Stylized facts of 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

Chapter 4 Measuring risks

4.1 Introduction

4.2 Synopsis of risk measures

4.3 Portfolio risk concepts

References

Chapter 5 Modern portfolio theory

5.1 Introduction

5.2 Markowitz portfolios

5.3 Empirical mean-variance portfolios

References

Part II Risk modelling

Chapter 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 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

Chapter 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 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 packages extRemes and in2extRemes

7.3.5 The package fExtremes

7.3.6 The package ismev

7.3.7 The package QRM

7.3.8 The packages Renext and RenextGUI

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

Chapter 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 GEVStableGarch

8.3.5 The package gogarch

8.3.6 The package lgarch

8.3.7 The packages rugarch and rmgarch

8.3.8 The package tseries

8.4 Empirical application of volatility models

References

Chapter 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 package copula

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

Part III Portfolio Optimization Approaches

Chapter 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 Packages for solving SOCPs

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

Chapter 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 package cccp

11.5.2 The packages DEoptim, DEoptimR, and RcppDE

11.5.3 The package FRAPO

11.5.4 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

Chapter 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

12.6.4 Risk surface plots

References

Chapter 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 The 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 PortfolioAnalytics

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 Entropy pooling

13.6.4 Protection strategies

References

Chapter 14 Probabilistic utility

14.1 Overview

14.2 The concept of probabilistic utility

14.3 Markov chain Monte Carlo

14.3.1 Introduction

14.3.2 Monte Carlo approaches

14.3.3 Markov chains

14.3.4 Metropolis-Hastings algorithm

14.4 Synopsis of R packages

14.4.1 Packages for conducting MCMC

14.4.2 Packages for analyzing MCMC

14.5 Empirical application

14.5.1 Exemplary utility function

14.5.2 Probabilistic versus maximized expected utility

14.5.3 Simulation of asset allocations

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 Irregularly 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 with databases

References

Appendix D Technicalities

Reference

Index

EULA