Financial Risk Modelling and Portfolio Optimization With R

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  • Format: Hardcover
  • Copyright: 2013-01-22
  • Publisher: Wiley
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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 techniques in modelling financial risks and applying portfolio optimisation techniques as well as recent advances in the field. Introduces stylised facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalised hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimisation with risk constraints. Enables the reader to replicate the results in the book using R code. Is accompanied by a supporting website featuring examples and case studies in R. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimisation will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

Table of Contents


List of Abbreviations

Part One Motivation

1 Introduction


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


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


4 Measuring Risks

4.1 Introduction

4.2 Synopsis of Risk Measures

4.3 Portfolio Risk Concepts


5 Modern Portfolio Theory

5.1 Introduction

5.2 Markowitz Portfolios

5.3 Empirical Mean-Variance Portfolios


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


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


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


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


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


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


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


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


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



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