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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Statistics in Practice
- Sprache: Englisch
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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Table of Contents
Cover
Title Page
Copyright
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
3.2 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
6.5 Synopsis of R packages for GLD
6.6 Applications of the GHD to risk modelling
6.7 Applications of the GLD to risk modelling and data analysis
References
Chapter 7: Extreme value theory
7.1 Preliminaries
7.2 Extreme value methods and models
7.3 Synopsis of R packages
7.4 Empirical applications of EVT
References
Chapter 8: Modelling volatility
8.1 Preliminaries
8.2 The class of ARCH models
8.3 Synopsis of R packages
8.4 Empirical application of volatility models
References
Chapter 9: Modelling dependence
9.1 Overview
9.2 Correlation, dependence, and distributions
9.3 Copulae
9.4 Synopsis of R packages
9.5 Empirical applications of copulae
References
Part III: Portfolio optimization approaches
Chapter 10: Robust portfolio optimization
10.1 Overview
10.2 Robust statistics
10.3 Robust optimization
10.4 Synopsis of R packages
10.5 Empirical applications
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
11.6 Empirical applications
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
12.6 Empirical applications
References
Chapter 13: Tactical asset allocation
13.1 Overview
13.2 Survey of selected time series models
13.3 The Black–Litterman approach
13.4 Copula opinion and entropy pooling
13.5 Synopsis of R packages
References
Chapter 14: Probabilistic utility
14.1 Overview
14.2 The concept of probabilistic utility
14.3 Markov chain Monte Carlo
14.4 Synopsis of R packages
14.5 Empirical application
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
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Part I: Motivation
Begin Reading
List of Illustrations
Chapter 3: Financial market data
Figure 3.1 Stylized facts for Siemens, part one.
Figure 3.2 Stylized facts for Siemens, part two.
Figure 3.3 European stock market data.
Figure 3.4 Cross-correlations between European stock market returns.
Figure 3.5 Rolling correlations of European stock markets.
Chapter 4: Measuring risks
Figure 4.1 Density of losses with VaR and ES.
Figure 4.2 Gaussian VaR, mVaR, and VaR of a skewed Student's
t
distribution.
Chapter 5: Modern portfolio theory
Figure 5.1 Global minimum variance and maximum Sharpe ratio portfolios.
Chapter 6: Suitable distributions for returns
Figure 6.1 Density function of the GHD class.
Figure 6.2 GLD: valid parameter combinations of and in non-shaded areas.
Figure 6.3 GLD shape plot.
Figure 6.4 Fitted densities for HWP returns.
Figure 6.5 QQ plot of fitted GHD for HWP returns.
Figure 6.6 Progression of VaR based upon GHD, HYP, and NIG models.
Figure 6.7 ES trajectory based upon GHD, HYP, and NIG models.
Figure 6.8 Shape triangle for HWP returns.
Figure 6.9 Back-test: VaR (99%) and losses for QCOM stock.
Figure 6.10 Shape triangle for FTSE 100 stock returns.
Chapter 7: Extreme value theory
Figure 7.1 Block maxima, largest orders, and peaks-over-threshold.
Figure 7.2 Block maxima for Siemens losses.
Figure 7.3 Diagnostic plots for fitted GEV model.
Figure 7.4 Profile log-likelihood plots for fitted GEV model.
Figure 7.5 Two largest annual losses of BMW.
Figure 7.6 Diagnostic plots for -block maxima: largest losses.
Figure 7.7 Diagnostic plots for -block maxima: PP and QQ plots.
Figure 7.8 MRL plot for Boeing losses.
Figure 7.9 Diagnostic plots of fitted GPD model.
Figure 7.10 Plots for clustering of NYSE exceedances.
Chapter 8: Modelling volatility
Figure 8.1 Plots of simulated white noise, ARCH(1), and ARCH(4) processes.
Figure 8.2 Time series plot for daily losses of NYSE.
Figure 8.3 Comparison of daily losses of NYSE and ES.
Chapter 9: Modelling dependence
Figure 9.1 Different types of copulae.
Figure 9.2 QQ plots for GARCH models.
Figure 9.3 Autocorrelations of squared standardized residuals.
Chapter 10: Robust portfolio optimization
Figure 10.1 Box-plots of stock index returns.
Figure 10.2 Relative performance of robust portfolios.
Figure 10.3 Efficient frontier of mean-variance and robust portfolios.
Chapter 11: Diversification reconsidered
Figure 11.1 Comparison of sector allocations.
Figure 11.2 Marginal risk contributions by sector per portfolio.
Figure 11.3 Marginal risk contributions by portfolio per sector.
Figure 11.4 Progression of out-of-sample portfolio wealth.
Figure 11.5 Relative out-performance of strategies versus S&P 500.
Chapter 12: Risk-optimal portfolios
Figure 12.1 Boundaries of mean-VaR portfolios in the mean-VaR plane.
Figure 12.2 Boundaries of mean-VaR portfolios in the mean standard deviation plane.
Figure 12.3 Tangency mean-VaR portfolio.
Figure 12.4 Discrete loss distribution: VaR, CVaR, , and .
Figure 12.5 Trajectory of minimum-CVaR and minimum-variance portfolio values.
Figure 12.6 Relative performance of minimum-CVaR and minimum-variance portfolios.
Figure 12.7 Draw-downs of GMV portfolio.
Figure 12.8 Comparison of draw-downs.
Figure 12.9 Comparison of wealth trajectories.
Figure 12.10 Comparison of draw-down trajectories.
Figure 12.11 Risk surface plot of marginal risk contributions with most diversified portfolio line.
Chapter 13: Tactical asset allocation
Figure 13.1 Tinbergen arrow diagram.
Figure 13.2 Identification: partial market model.
Figure 13.3 Equity for BL, prior, and equal-weighted portfolios.
Figure 13.4 Box-plots of weights based on prior and BL distributions.
Figure 13.5 Prior and posterior densities.
Figure 13.6 Wealth trajectories.
Figure 13.7 Trajectories of stock index values in euros.
Figure 13.8 Progression of portfolio wealth.
Chapter 14: Probabilistic utility
Figure 14.1 Concept of probabilistic utility: one risky asset.
Figure 14.2 Probabilistic utility: asymptotic property for .
Figure 14.3 Monte Carlo integration: progression of estimates with two-sided error bands.
Figure 14.4 Accept–reject sampling: graphical representation.
Figure 14.5 Progression of first 100 observations of a Markov chain process.
Figure 14.6 Metropolis–Hastings: comparison of independence and random walk sampler.
Figure 14.7 Graphical analysis of utility simulation.
List of Tables
Chapter 6: Suitable distributions for returns
Table 6.1 Range of valid GLD parameter combinations
Table 6.2 Results of distributions fitted to HWP returns
Chapter 7: Extreme value theory
Table 7.1 Fitted GEV to block maxima of Siemens
Table 7.2 Fitted -block maxima of BMW
Table 7.3 Fitted GPD of Boeing
Table 7.4 Risk measures for Boeing
Table 7.5 Results for declustered GPD models
Chapter 8: Modelling volatility
Table 8.1 Overview of package
gogarch
Chapter 9: Modelling dependence
Table 9.1 Fitted GARCH models
Table 9.2 Fitted mix of Clayton and Gumbel
Chapter 10: Robust portfolio optimization
Table 10.1 Portfolio simulation: summary of portfolio risks
Table 10.2 Portfolio back-test: descriptive statistics of returns
Table 10.3 Portfolio back-test: descriptive statistics of excess returns
Chapter 11: Diversification reconsidered
Table 11.1 Key measures of portfolio solutions for Swiss equity sectors
Table 11.2 Key measures of portfolio solutions for S&P 500
Table 11.3 Weight and downside risk contributions of multi-asset portfolios
Table 11.4 Key measures of portfolio solutions for multi-asset portfolios
Chapter 12: Risk-optimal portfolios
Table 12.1 Comparison of portfolio allocations and characteristics
Table 12.2 Overview of draw-downs (positive, percentages)
Table 12.3 Performance statistics
Table 12.4 Feasible asset allocations with similar degree of risk concentration
Chapter 13: Tactical asset allocation
Table 13.1 Structure of package
vars
Table 13.2 Test statistics for unit root tests
Table 13.3 Results of maximum eigenvalue test for VECM
Table 13.4 Comparison of portfolio allocations
Table 13.5 Key portfolio performance measures
Table 13.6 Key portfolio performance measures
Chapter 14: Probabilistic utility
Table 14.1 Probabilistic utility: comparison of allocations
Table 14.2 Probabilistic utility versus maximum expected utility: mean allocations
Table 14.3 Probabilistic utility versus maximum expected utility: span of weights
Financial Risk Modelling and Portfolio Optimization with R
Second Edition
Bernhard Pfaff
This edition first published 2016
© 2016, John Wiley & Sons, Ltd
First Edition published in 2013
Registered office
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom
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The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.
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Library of Congress Cataloging-in-Publication Data applied for
ISBN : 9781119119661
A catalogue record for this book is available from the British Library.
Cover Image: R logo © 2016 The R Foundation. Creative Commons Attribution-ShareAlike 4.0 International license (CC-BY-SA 4.0).
Preface to the Second Edition
Roughly three years have passed since the first edition, during which episodes of higher risk environments in the financial market could be observed. Instances thereof are, for example, due to the abandoning of the Swiss franc currency ceiling with respect to the euro, the decrease in Chinese stock prices, and the Greek debt crisis; and these all happened just during the first three quarters of 2015. Hence, the need for a knowledge base of statistical techniques and portfolio optimization approaches for addressing financial market risk appropriately has not abated.
This revised and enlarged edition was also driven by a need to update certain R code listings to keep pace with the latest package releases. Furthermore, topics such as the concept of reference classes in R (see Section 2.4), risk surface plots (see Section 12.6.4), and the concept of probabilistic utility optimization (see Chapter 14) have been added, though the majority of the book and its chapters remain unchanged. That is, in each chapter certain methods and/or optimization techniques are introduced formally, followed by a synopsis of relevant R packages, and finally the techniques are elucidated by a number of examples.
Of course, the book's accompanying package FRAPO has also been refurbished (version ). Not only have the R code examples been updated, but the routines for portfolio optimization cast with a quadratic objective function now utilize the facilities of the cccp package. The package is made available on CRAN. Furthermore, the URL of the book's accompanying website remains unchanged and can be accessed from www.pfaffikus.de.
Bernhard PfaffKronberg im Taunus
Preface
The project for this book commenced in mid-2010. At that time, financial markets were in distress and far from operating smoothly. The impact of the US real-estate crisis could still be felt and the sovereign debt crisis in some European countries was beginning to emerge. Major central banks implemented measures to avoid a collapse of the inter-bank market by providing liquidity. Given the massive financial book and real losses sustained by investors, it was also a time when quantitatively managed funds were in jeopardy and investors questioned the suitability of quantitative methods for protecting their wealth from the severe losses they had made in the past.
Two years later not much has changed, though the debate on whether quantitative techniques per se are limited has ceased. Hence, the modelling of financial risks and the adequate allocation of wealth is still as important as it always has been, and these topics have gained in importance, driven by experiences since the financial crisis started in the latter part of the previous decade.
The content of the book is aimed at these two topics by acquainting and familiarizing the reader with market risk models and portfolio optimization techniques that have been proposed in the literature. These more recently proposed methods are elucidated by code examples written in the R language, a freely available software environment for statistical computing.
This book certainly could not have been written without the public provision of such a superb piece of software as R, and the numerous package authors who have greatly enriched this software environment. I therefore wish to express my sincere appreciation and thanks to the R Core team members and all the contributors and maintainers of the packages cited and utilized in this book. By the same token, I would like to apologize to those authors whose packages I have not mentioned. This can only be ascribed to my ignorance of their existence. Second, I would like to thank John Wiley & Sons Ltd for the opportunity to write on this topic, in particular Ilaria Meliconi who initiated this book project in the first place and Heather Kay and Richard Davies for their careful editorial work. Special thanks belongs to Richard Leigh for his meticulous and mindful copy-editing. Needless to say, any errors and omissions are entirely my responsibility. Finally, I owe a debt of profound gratitude to my beloved wife, Antonia, who while bearing the burden of many hours of solitude during the writing of this book remained a constant source of support.
This book includes an accompanying website. Please visit www.wiley.com/go/financial_risk.
Bernhard PfaffKronberg im Taunus
Abbreviations
ACF
Autocorrelation function
ADF
Augmented Dickey–Fuller
AIC
Akaike information criterion
AMPL
A modelling language for mathematical programming
ANSI
American National Standards Institute
APARCH
Asymmetric power ARCH
API
Application programming interface
ARCH
Autoregressive conditional heteroskedastic
AvDD
Average draw-down
BFGS
Broyden–Fletcher–Goldfarb–Shanno algorithm
BL
Black–Litterman
BP
Break point
CDaR
Conditional draw-down at risk
CLI
Command line interface
CLT
Central limit theorem
CML
Capital market line
COM
Component object model
COP
Copula opinion pooling
CPPI
Constant proportion portfolio insurance
CRAN
Comprehensive R archive network
CVaR
Conditional value at risk
DBMS
Database management system
DE
Differential evolution
DGP
Data-generation process
DR
Diversification ratio
EDA
Exploratory data analysis
EGARCH
Exponential GARCH
EP
Entropy pooling
ERS
Elliott–Rothenberg–Stock
ES
Expected shortfall
EVT
Extreme value theory
FIML
Full-information maximum likelihood
GARCH
Generalized autoregressive conditional heteroskedastic
GEV
Generalized extreme values
GHD
Generalized hyperbolic distribution
GIG
Generalized inverse Gaussian
GLD
Generalized lambda distribution
GLPK
GNU Linear Programming Kit
GMPL
GNU MathProg modelling language
GMV
Global minimum variance
GOGARCH
Generalized orthogonal GARCH
GPD
Generalized Pareto distribution
GPL
GNU Public License
GUI
Graphical user interface
HYP
Hyperbolic
IDE
Integrated development environment
iid
independently, identically distributed
JDBC
Java database connectivity
LP
Linear program
MaxDD
Maximum draw-down
MCD
Minimum covariance determinant
MCMC
Markov chain Monte Carlo
MDA
Maximum domain of attraction
mES
Modified expected shortfall
MILP
Mixed integer linear program
ML
Maximum likelihood
MPS
Mathematical programming system
MRC
Marginal risk contributions
MRL
Meanresidual life
MSR
Maximum Sharpe ratio
mVaR
Modified value at risk
MVE
Minimum volume ellipsoid
NIG
Normal inverse Gaussian
NN
Nearest neighbour
OBPI
Option-based portfolio insurance
ODBC
Open database connectivity
OGK
Orthogonalized Gnanadesikan–Kettenring
OLS
Ordinary least squares
OO
Object-oriented
PACF
Partial autocorrelation function
POT
Peaks over threshold
PWM
Probability-weighted moments
QMLE
Quasi-maximum-likelihood estimation
RDBMS
Relational database management system
RE
Relative efficiency
RPC
Remote procedure call
SDE
Stahel–Donoho estimator
SIG
Special interest group
SMEM
Structural multiple equation model
SPI
Swiss performance index
SVAR
Structural vector autoregressive model
SVEC
Structural vector error correction model
TAA
Tactical asset allocation
TDC
Tail dependence coefficient
VAR
Vector autoregressive model
VaR
Value at risk
VECM
Vector error correction model
XML
Extensible markup language
Unless otherwise stated, the following notation, symbols, and variables are used.
Notation
Lower case in bold:
Vectors
Upper case:
Matrices
Greek letters:
Scalars
Greek letters with ˆ or ∼ or ¯
Sample values (estimates or estimators)
Symbols and variables
Absolute value of an expression
Distributed according to
Kronecker product of two matrices
Maximum value of an argument
Minimum value of an argument
Complement of a matrix
Copula
Correlation(s) of an expression
Covariance of an expression
Draw-down
Determinant of a matrix
Expectation operator
Information set
Integrated of order
Lag operator
(Log-)likelihood function
Expected value
Normal distribution
Weight vector
Portfolio problemspecification
Probability expression
Variance-covariance matrix
Standard deviation
Variance
Uncertainty set
Variance of an expression
About the Companion Website
Don't forget to visit the companion website for this book:
www.pfaffikus.de
There you will find valuable material designed to enhance your learning, including:
All R code examples
The
FRAPO
R package.
Scan this QR code to visit the companion website.
Part IMotivation
Chapter 1Introduction
The period since the late 1990s has been marked by financial crises—the Asian crisis of 1997, the Russian debt crisis of 1998, the bursting of the dot-com bubble in 2000, the crises following the attack on the World Trade Center in 2001 and the invasion of Iraq in 2003, the sub-prime mortgage crisis of 2007, and European sovereign debt crisis since 2009 being the most prominent. All of these crises had a tremendous impact on the financial markets, in particular an upsurge in observed volatility and massive destruction of financial wealth. During most of these episodes the stability of the financial system was in jeopardy and the major central banks were more or less obliged to take countermeasures, as were the governments of the relevant countries. Of course, this is not to say that the time prior to the late 1990s was tranquil—in this context we may mention the European Currency Unit crisis in 1992–1993 and the crash on Wall Street in 1987, known as Black Monday. However, it is fair to say that the frequency of occurrence of crises has increased during the last 15 years.
Given this rise in the frequency of crises, the modelling and measurement of financial market risk have gained tremendously in importance and the focus of portfolio allocation has shifted from the side of the coin to the side. Hence, it has become necessary to devise and employ methods and techniques that are better able to cope with the empirically observed extreme fluctuations in the financial markets. The hitherto fundamental assumption of independent and identically normally distributed financial market returns is no longer sacrosanct, having been challenged by statistical models and concepts that take the occurrence of extreme events more adequately into account than the Gaussian model assumption does. As will be shown in the following chapters, the more recently proposed methods of and approaches to wealth allocation are not of a revolutionary kind, but can be seen as an evolutionary development: a recombination and application of already existing statistical concepts to solve finance-related problems. Sixty years after Markowitz's seminal paper “Modern Portfolio Theory,” the key paradigm must still be considered as the anchor for portfolio optimization. What has been changed by the more recently advocated approaches, however, is how the riskiness of an asset is assessed and how portfolio diversification, that is, the dependencies between financial instruments, is measured, and the definition of the portfolio's objective per se.
The purpose of this book is to acquaint the reader with some of these recently proposed approaches. Given the length of the book this synopsis must be selective, but the topics chosen are intended to cover a broad spectrum. In order to foster the reader's understanding of these advances, all the concepts introduced are elucidated by practical examples. This is accomplished by means of the R language, a free statistical computing environment (see R Core Team 2016). Therefore, almost regardless of the reader's computer facilities in terms of hardware and operating system, all the code examples can be replicated at the reader's desk and s/he is encouraged not only to do so, but also to adapt the code examples to her/his own needs. This book is aimed at the quantitatively inclined reader with a background in finance, statistics, and mathematics at upper undergraduate/graduate level. The text can also be used as an accompanying source in a computer lab class, where the modelling of financial risks and/or portfolio optimization are of interest.
The book is divided into three parts. The chapters of this first part are primarily intended to provide an overview of the topics covered in later chapters and serve as motivation for applying techniques beyond those commonly encountered in assessing financial market risks and/or portfolio optimization. Chapter 2 provides a brief course in the R language and presents the FRAPO package that accompanies the book. For the reader completely unacquainted with R, this chapter cannot replace a more dedicated course of study of the language itself, but it is rather intended to provide a broad overview of R and how to obtain help. Because in the book's examples quite a few R packages will be presented and utilized, a section on the existing classes and methods is included that will ease the reader's comprehension of these two frameworks. In Chapter 3, stylized facts of univariate and multivariate financial market data are presented. The exposition of these empirical characteristics serves as motivation for the methods and models presented in Part II. Definitions used in the measurement of financial market risks at the single-asset and portfolio level are the topic of the Chapter 4. In the final chapter of Part I (Chapter 5), the Markowitz portfolio framework is described and empirical artifacts of the accordingly optimized portfolios are presented. The latter serve as motivation for the alternative portfolio optimization techniques presented in Part III.
In Part II, alternatives to the normal distribution assumption for modelling and measuring financial market risks are presented. This part commences with an exposition of the generalized hyperbolic and generalized lambda distributions for modelling returns of financial instruments. In Chapter 7, the extreme value theory is introduced as a means of modelling and capturing severe financial losses. Here, the block-maxima and peaks-over-threshold approaches are described and applied to stock losses. Both Chapters 6 and 7 have the unconditional modelling of financial losses in common. The conditional modelling and measurement of financial market risks is presented in the form of GARCH models—defined in the broader sense—in Chapter 8. Part II concludes with a chapter on copulae as a means of modelling the dependencies between assets.
Part III commences by introducing robust portfolio optimization techniques as a remedy to the outlier sensitivity encountered by plain Markowitz optimization. In Chapter 10 it is shown how robust estimators for the first and second moments can be used as well as portfolio optimization methods that directly facilitate the inclusion of parameter uncertainty. In Chapter 11 the concept of portfolio diversification is reconsidered. In this chapter the portfolio concepts of the most diversified, equal risk contributed and minimum tail-dependent portfolios are described. In Chapter 12 the focus shifts to downside-related risk measures, such as the conditional value at risk and the draw-down of a portfolio. Chapter 13 is devoted to tactical asset allocation (TAA). Aside from the original Black–Litterman approach, the concept of copula opinion pooling and the construction of a wealth protection strategy are described. The latter is a synthesis between the topics presented in Part II and TAA-related portfolio optimization.
In Appendix A all the R packages cited and used are listed by name and topic. Due to alternative means of handling longitudinal data in R, a separate chapter (Appendix B) is dedicated to the presentation of the available classes and methods. Appendix C shows how R can be invoked and employed on a regular basis for producing back-tests, utilized for generating or updating reports, and/or embedded in an existing IT infrastructure for risk assessment/portfolio rebalancing. Because all of these topics are highly application-specific, only pointers to the R facilities are provided. A section on the technicalities concludes the book.
The chapters in Parts II and III adhere to a common structure. First, the methods and/or models are presented from a theoretical viewpoint only. The following section is reserved for the presentation of R packages, and the last section in each chapter contains applications of the concepts and methods previously presented. The R code examples provided are written at an intermediate language level and are intended to be digestible and easy to follow. Each code example could certainly be improved in terms of profiling and the accomplishment of certain computations, but at the risk of too cryptic a code design. It is left to the reader as an exercise to adapt and/or improve the examples to her/his own needs and preferences.
All in all, the aim of this book is to enable the reader to go beyond the ordinarily encountered standard tools and techniques and provide some guidance on when to choose among them. Each quantitative model certainly has its strengths and drawbacks and it is still a subjective matter whether the former outweigh the latter when it comes to employing the model in managing financial market risks and/or allocating wealth at hand. That said, it is better to have a larger set of tools available than to be forced to rely on a more restricted set of methods.
Reference
R Core Team 2016
R: A Language and Environment for Statistical Computing
R Foundation for Statistical Computing Vienna, Austria.
