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:

> install.packages('zoo')

After installation, the package should be also loaded first to the current R session before usage:

> library(zoo)

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:

logreturn <- function(x) {
    log(tail(x, -1) / head(x, -1))
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

logreturn <- function(x) {
    log(tail(x, -1) / head(x, -1))
}

Any command-line input or output is written as follows:

> pi
[1] 3.141593

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

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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