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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.
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Seitenzahl: 179
Veröffentlichungsjahr: 2013
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Table of Contents
Introduction to R for Quantitative Finance
Introduction to R for Quantitative Finance
Copyright © 2013 Packt Publishing
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First published: November 2013
Production Reference: 1151113
Published by Packt Publishing Ltd.
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ISBN 978-1-78328-093-3
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Cover Image by Suresh Mogre (<[email protected]>)
Credits
Authors
Gergely Daróczi
Michael Puhle
Edina Berlinger
Péter Csóka
Dániel Havran
Márton Michaletzky
Zsolt Tulassay
Kata Váradi
Agnes Vidovics-Dancs
Reviewers
Dr. Hari Shanker Gupta
Ronald Hochreiter
Acquisition Editor
Akram Hussain
Lead Technical Editor
Mohammed Fahad
Technical Editors
Venu Manthena
Adrian Raposo
Amit Singh
Copy Editors
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Lavina Pereira
Project Coordinator
Sherin Padayatty
Proofreader
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Indexer
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Graphics
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Abinash Sahu
Production Coordinator
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Cover Work
Kirtee Shingan
About the Authors
Gergely Daróczi is a Ph.D. candidate in Sociology with around eight years' experience in data management and analysis tasks within the R programming environment. Besides teaching Statistics at different Hungarian universities and doing data analysis jobs for several years, Gergely has founded and coordinated a UK-based online reporting startup company recently. This latter software or platform as a service which is called rapporter.net will potentially provide an intuitive frontend and an interface to all the methods and techniques covered in the book. His role in the book was to provide R implementation of the QF problems and methods.
I am more than grateful to the members of my little family for their support and understanding, even though they missed me a lot while I worked on the R parts of this book. I am also really thankful to all the co-authors who teach at the Corvinus University of Budapest, Hungary, for providing useful content for this co-operation.
Michael Puhle obtained a Ph.D. in Finance from the University of Passau in Germany. He worked for several years as a Senior Risk Controller at Allianz Global Investors in Munich, and as an Assistant Manager at KPMG's Financial Risk Management practice, where he was advising banks on market risk models. Michael is also the author of Bond Portfolio Optimization published by Springer Publishing.
Edina Berlinger has a Ph.D. in Economics from the Corvinus University of Budapest. She is an Associate Professor, teaching corporate finance, investments, and financial risk management. She is the Head of Department for Finance of the university and is also the Chair of the Finance Sub committee the Hungarian Academy of Sciences. Her expertise covers student loan systems, risk management, and, recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk.
Péter Csóka is an Associate Professor at the Department of Finance, Corvinus University of Budapest, and a research fellow in the Game Theory Research Group, Centre For Economic and Regional Studies, Hungarian Academy of Sciences. He received his Ph.D. in Economics from Maastricht University in 2008. His research topics include risk measures, risk capital allocation, game theory, corporate finance, and general equilibrium theory. He is currently focused on analyzing risk contributions for systemic risk and for illiquid portfolios. He has papers published in journals such as Mathematical Methods of Operational Research, European Journal of Operational Research, Games and Economic Behaviour, and Journal of Banking and Finance. He is the Chair of the organizing committee of the Annual Financial Market Liquidity Conference in Budapest.
Daniel Havran is a Post Doctoral Fellow at the Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences. He also holds a part-time Assistant Professorship position at the Corvinus University of Budapest, where he teaches Corporate Finance (BA and Ph.D. levels), and Credit Risk Management (MSc) courses. He obtained his Ph.D. in Economics at Corvinus University of Budapest in 2011. His research interests are corporate cash, funding liquidity management, and credit derivatives over-the-counter markets.
Márton Michaletzky obtained his Ph.D. degree in Economics in 2011 from Corvinus University of Budapest. Between 2000 and 2003, he has been a Risk Manager and Macroeconomic Analyst with Concorde Securities Ltd. As Capital Market Transactions Manager, he gained experience in an EUR 3 bn securitization at the Hungarian State Motorway Management Company. In 2012, he took part in the preparation of an IPO and the private placement of a Hungarian financial services provider. Prior to joining DBH Investment, he was an assistant professor at the Department of Finance of CUB.
Zsolt Tulassay works as a Quantitative Analyst at a major US investment bank, validating derivatives pricing models. Previously, Zsolt worked as an Assistant Lecturer at the Department of Finance at Corvinus University, teaching courses on Derivatives, Quantitative Risk Management, and Financial Econometrics. Zsolt holds MA degrees in Economics from Corvinus University of Budapest and Central European University. His research interests include derivatives pricing, yield curve modeling, liquidity risk, and heterogeneous agent models.
Kata Váradi is an Assistant Professor at the Department of Finance, Corvinus University of Budapest since 2013. Kata graduated in Finance in 2009 from Corvinus University of Budapest, and was awarded a Ph.D. degree in 2012 for her thesis on the analysis of the market liquidity risk on the Hungarian stock market. Her research areas are market liquidity, fixed income securities, and networks in healthcare systems. Besides doing research, she is active in teaching as well. She teaches mainly Corporate Finance, Investments, Valuation, and Multinational Financial Management.
Agnes Vidovics-Dancs is a Ph.D. candidate and an Assistant Professor at the Department of Finance, Corvinus University of Budapest. Previously, she worked as a Junior Risk Manager in the Hungarian Government Debt Management Agency. Her main research areas are government debt management in general, especially sovereign crises and defaults.
About the Reviewers
Dr. Hari Shanker Gupta is a Quantitative Research Analyst working in the area of Algorithming Trading System Development. Prior to this, he was a Post Doctoral Fellow at Indian Institute of Science (IISc), Bangalore, India. Hari has pursued his Ph.D. from Department of Mathematics, IISc, in the field of Applied Mathematics and Scientific Computation in the year 2010. Hari had completed his M.Sc. in Mathematics from Banaras Hindu University (B.H.U.), Varanasi, India. During M.Sc., Hari was awarded four gold medals for his outstanding performance in B.H.U., Varanasi.
Hari has published five research papers in reputed journals in the field of Mathematics and Scientific Computation. He has experience of working in the areas of mathematics, statistics, and computations. These include the topics: numerical methods, partial differential equation, mathematical finance, stochastic calculus, data analysis, finite difference, and finite element method. He is very comfortable with the mathematics software, Matlab; the statistics programming language, R, and, the programming language, C, and has been recently working on the Python platform.
Ronald Hochreiter is an Assistant Professor at the Department of Finance, Accounting and Statistics, at the WU Vienna University of Economics and Business. He obtained his Ph.D. in Computational Management Science at the University of Vienna in 2005. He is an avid R user and develops R packages mainly for optimization modeling purposes as well as for applications in Finance. A summary of his R projects can be found at http://www.hochreiter.net/R/, and some of his tutorials on Financial Engineering with R are online at http://www.finance-r.com/.
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Preface
Introduction to R for Quantitative Finance will show you how to solve real-world quantitative finance problems using the statistical computing languages R and QF. In this book, we will cover diverse topics ranging from Time Series Analysis to Financial Networks. Each chapter will briefly present the theory and deal with solving a specific problem using R.
What this book covers
Chapter 1, Time Series Analysis (Michael Puhle), explains working with time series data in R. Furthermore, you will learn how to model and forecast house prices, improve hedge ratios using cointegration, and model volatility.
Chapter 2, Portfolio Optimization (Péter Csóka, Ferenc Illés, Gergely Daróczi), covers the theoretical idea behind portfolio selection and shows how to apply this knowledge to real-world data.
Chapter 3, Asset Pricing Models (Kata Váradi, Barbara Mária Dömötör, Gergely Daróczi), builds on the previous chapter and presents models for the relationship between asset return and risk. We'll cover the Capital Asset Pricing Model and the Arbitrage Pricing Theory.
Chapter 4, Fixed Income Securities (Márton Michaletzky, Gergely Daróczi), deals with the basics of fixed income instruments. Furthermore, you will learn how to calculate the risk of such an instrument and construct portfolios that will be immune to changes in interest rates.
Chapter 5, Estimating the Term Structure of Interest Rates (Tamás Makara, Gergely Daróczi), introduces the concept of a yield curve and shows how to estimate it using prices of government bonds.
Chapter 6, Derivatives Pricing (Ágnes Vidovics-Dancs, Gergely Daróczi), explains the pricing of derivatives using discrete and continuous time models. Furthermore, you will learn how to calculate derivatives risk measures and the so-called "Greeks".
Chapter 7, Credit Risk Management (Dániel Havran, Gergely Daróczi), gives an introduction to the credit default models and shows how to model correlated defaults using copulas.
Chapter 8, Extreme Value Theory (Zsolt Tulassay), presents possible uses of Extreme Value Theory in insurance and finance. You will learn how to fit a model to the tails of the distribution of fire losses. Then we will use the fitted model to calculate Value-at-Risk and Expected Shortfall.
Chapter 9, Financial Networks (Edina Berlinger, Gergely Daróczi), explains how financial networks can be represented, simulated, visualized, and analyzed in R. We will analyze the interbank lending market and learn how to systemically detect important financial institutions.
What you need for this book
All the code examples provided in this book should be run in the R console that is to be installed first on a computer. You can download the software for free and find the installation instructions for all major operating systems at http://r-project.org. Although we will not cover advanced topics such as how to use R in Integrated Development Environments, there are awesome plugins for Emacs, Eclipse, vi, or Notepad++ besides other editors, and we can also highly recommend trying RStudio, which is a free and open source IDE dedicated to R.
Apart from a working R installation, we will also use some user-contributed R packages that can be easily installed from the Comprehensive R Archive Network. To install a package, use the install.packages command in the R console, shown as follows:
After installation, the package should be also loaded first to the current R session before usage:
You can find free introductory articles and manuals on the R homepage, but this book is targeted towards beginners, so no additional R knowledge is assumed from the reader.
Who this book is for
The book is aimed at readers who wish to use R to solve problems in quantitative finance. Some familiarity with finance is assumed, but we generally provide the financial theory as well. Familiarity with R is not assumed. Those who want to get started with R may find this book useful as we don't give a complete overview of the R language but show how to use parts of it to solve specific problems. Even if you already use R, you will surely be amazed to see the wide range of problems that it can be applied to.
Conventions
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "we will employ some methods from the forecast package"
A block of R code (usually a function's body) is set as follows:
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
Any command-line input or output is written as follows:
Where ">" shows that the R console is waiting for commands to be evaluated. Multiline expressions are started with the same symbol on the first line, but all the rest lines have a "+" sign at the beginning to show that the last R expression is still to be finished.
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking the Next button moves you to the next screen".
Note
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Tip
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Chapter 1. Time Series Analysis
Time series analysis is concerned with the analysis of data collected over time. Adjacent observations are typically dependent. Time series analysis hence deals with techniques for the analysis of this dependence.
The objective of this chapter is to introduce some common modeling techniques by means of specific applications. We will see how to use R to solve these real-world examples. We begin with some thoughts about how to store and process time series data in R. Afterwards, we deal with linear time series analysis and how it can be used to model and forecast house prices. In the subsequent section, we use the notion of cointegration to improve on the basic minimal variance hedge ratio by taking long-run trends into consideration. The chapter concludes with a section on how to use volatility models for risk management purposes.
Working with time series data
The native R classes suitable for storing time series data include vector, matrix, data.frame, and ts objects. But the types of data that can be stored in these objects are narrow; furthermore, the methods provided by these representations are limited in scope. Luckily, there exist specialized objects that deal with more general representation of time series data: zoo, xts, or timeSeries objects, available from packages of the same name.
It is not necessary to create time series objects for every time series analysis problem, but more sophisticated analyses require time series objects. You could calculate the mean or variance of time series data represented as a vector in R, but if you want to perform a seasonal decomposition using decompose, you need to have the data stored in a time series object.
