Portfolio Optimization with R/Rmetrics

Download

Diethelm Würtz, Tobias Setz, Yohan Chalabi, William Chen, Andrew Ellis
Rmetrics eBooks 2009, NEW: Update 2015
Rmetrics Association and Finance Online Publishing, Zurich
455 Pages, 87 Figures
ISBN: 978-3-906041-01-8

Google Books Preview
REvolution Book Review

About the Book:

This is a book about portfolio optimization from the perspective of computational finance and financial engineering. Thus the main emphasis is to briefly introduce the concepts and to give the reader a set of powerful tools to solve the problems in the field of portfolio optimization. This book divides roughly into five parts. The first part, Chapters 1-10, is dedicated to the exploratory data analysis of financial assets, the second part, Chapters 11-14, to the framework of portfolio design, selection and optimization, the third part, Chapters 15-19, to the mean-variance portfolio approach, the fourth part, Chapters 20-23, to the mean-conditional value-at-risk portfolio approach, and the fifth part, Chapters 24-26, to portfolio backtesting and benchmarking.

The NEW Update 2015 supports R Version 3.2.

About the Authors:

Diethelm Würtz is Professor at the “Swiss Federal Institute of Technology”
(ETH) in Zurich. Diethelm is teaching regular ETH lectures and seminars in Computational Finance and Financial Engineering. He is involved in the organization of the “Rmetrics Summer Schools” and in several international workshops, courses and meetings in Europe and Asia. He is President of the Open Source “Rmetrics Association”, Senior Partner of “Finance Online GmbH” and Co-Founder of “Sidenis AG” in Zurich.

Tobias Setz holds a master in Computational Science and Engineering from ETH in Zurich with a major specialization in theoretical physics and a minor specialization in financial engineering. Currently he is doing his PhD in the Econophysics group of Prof Diethelm Würtz. In his theses he focused on stability indicators to describe the condition of financial or economic markets or to improve trading strategies. Besides this academic work, he is also a developer of the R/Rmetrics packages covering time series analysis and portfolio optimization (www.rmetrics.org).

William Chen has a master in statistics from University of Auckland in New Zealand. In the summer of 2008, he did a Student Internship in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics. During his three months internship,William contributed to the portfolio backtest package.

Andrew Ellis read neuroscience and mathematics at the University in Zurich. He did a Student Internship in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics. Andrew is worked on the Rmetrics documentation project and co-authored this ebook on portfolio optimization with Rmetrics.

Yohan Chalabi has a master in Physics from the Swiss Federal Institute of Technology in Lausanne. He made his Doctorat Degree in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics. Yohan is a co-author of the Rmetrics packages.

Table of Contents: Download extract

Introduction

Part One: Managing Data Sets of Assets

Introduction Generic Functions to Manipulate Assets

Financial Functions to Manipulate Assets

Basic Statistics of Financial Assets

Robust Mean and Covariance Estimates of Assets

Part Two: Exploratory Data Analysis of Assets

Introduction

Financial Time Series And Their Properties

Customization of Plots

Modelling Assets Returns

Selecting Similar or Dissimilar Assets

Comparing Multivariate Return and Risk Statistics

Pairwise Dependencies of Assets

Part Three: Portfolio Framework

Introduction

S4 Portfolio Specification Class

S4 Portfolio Data Class

S4 Portfolio Constraints Class

Portfolio Functions

Part Four: Mean-Variance Portfolio

Markowitz Portfolio Theory

Mean-Variance Portfolio Settings

Minimum Risk Mean-Variance Portfolios

Mean-Variance Portfolio Frontiers

Case Study: Dow Jones Index

Robust Portfolios and Covariance Estimation

Part Five: Mean-CVaR Portfolios

Introduction

Mean-CVaR Portfolio

Theory Mean-CVaR Portfolio Settings

Mean-CVaR Portfolios

Mean-CVaR Portfolio Frontiers

Part Six: Portfolio Backtesting

Introduction

S4 Portfolio Backtest Class

Case Study: SPI Sector Rotation

Case Study: GCC Index Rotation

Part Seven: Appendix

Download