Bernhard Pfaff

Financial Risk Modelling and Portfolio Optimization with R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: * Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. * Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. * Explores portfolio risk concepts and optimization with risk constraints. * Is accompanied by a supporting website featuring examples and case studies in R. * Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 448
• Erscheinungstermin: 16. August 2016
• Englisch
• ISBN-13: 9781119119685
• Artikelnr.: 45681792

Autorenporträt

Bernhard Eugen Heinrich Pfaff, Director, Invesco Asset Management Deutschland GmbH, Germany.

Inhaltsangabe

Preface to the Second Edition xi

Preface xiii

Abbreviations xv

About the Companion Website xix

PART I MOTIVATION 1

1 Introduction 3

Reference 5

2 A brief course in R 6

2.1 Origin and development 6

2.2 Getting help 7

2.3 Working with R 10

2.4 Classes, methods, and functions 12

2.5 The accompanying package FRAPO 22

References 28

3 Financial market data 29

3.1 Stylized facts of financial market returns 29

3.1.1 Stylized facts for univariate series 29

3.1.2 Stylized facts for multivariate series 32

3.2 Implications for risk models 35

References 36

4 Measuring risks 37

4.1 Introduction 37

4.2 Synopsis of risk measures 37

4.3 Portfolio risk concepts 42

References 44

5 Modern portfolio theory 46

5.1 Introduction 46

5.2 Markowitz portfolios 47

5.3 Empirical mean-variance portfolios 50

References 52

PART II RISK MODELLING 55

6 Suitable distributions for returns 57

6.1 Preliminaries 57

6.2 The generalized hyperbolic distribution 57

6.3 The generalized lambda distribution 60

6.4 Synopsis of R packages for GHD 66

6.4.1 The package fBasics 66

6.4.2 The package GeneralizedHyperbolic 67

6.4.3 The package ghyp 69

6.4.4 The package QRM 70

6.4.5 The package SkewHyperbolic 70

6.4.6 The package VarianceGamma 71

6.5 Synopsis of R packages for GLD 71

6.5.1 The package Davies 71

6.5.2 The package fBasics 72

6.5.3 The package gld 73

6.5.4 The package lmomco 73

6.6 Applications of the GHD to risk modelling 74

6.6.1 Fitting stock returns to the GHD 74

6.6.2 Risk assessment with the GHD 77

6.6.3 Stylized facts revisited 80

6.7 Applications of the GLD to risk modelling and data analysis 82

6.7.1 VaR for a single stock 82

6.7.2 Shape triangle for FTSE 100 constituents 84

References 86

7 Extreme value theory 89

7.1 Preliminaries 89

7.2 Extreme value methods and models 90

7.2.1 The block maxima approach 90

7.2.2 The rth largest order models 91

7.2.3 The peaks-over-threshold approach 92

7.3 Synopsis of R packages 94

7.3.1 The package evd 94

7.3.2 The package evdbayes 95

7.3.3 The package evir 96

7.3.4 The packages extRemes and in2extRemes 98

7.3.5 The package fExtremes 99

7.3.6 The package ismev 101

7.3.7 The package QRM 101

7.3.8 The packages Renext and RenextGUI 102

7.4 Empirical applications of EVT 103

7.4.1 Section outline 103

7.4.2 Block maxima model for Siemens 103

7.4.3 r-block maxima for BMW 107

7.4.4 POT method for Boeing 110

References 115

8 Modelling volatility 116

8.1 Preliminaries 116

8.2 The class of ARCH models 116

8.3 Synopsis of R packages 120

8.3.1 The package bayesGARCH 120

8.3.2 The package ccgarch 121

8.3.3 The package fGarch 122

8.3.4 The package GEVStableGarch 122

8.3.5 The package gogarch 123

8.3.6 The package lgarch 123

8.3.7 The packages rugarch and rmgarch 125

8.3.8 The package tseries 127

8.4 Empirical application of volatility models 128

References 130

9 Modelling dependence 133