Title:
Financial risk modelling and portfolio optimization with R
Personal Author:
Series:
Statistics in practice
Publication Information:
Hoboken, N.J. : Wiley, 2013
Physical Description:
xvi, 356 p. : ill. ; 24 cm.
ISBN:
9780470978702
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010307170 | HG106 P484 2013 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
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.
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. 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 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.
Table of Contents
| Preface |
| List of Abbreviations |
| Part 1 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 2 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 3 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 |
| 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 |
