#### Overview of this book

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.
Introduction to R for Quantitative Finance
Credits
www.PacktPub.com
Preface
Free Chapter
Time Series Analysis
Portfolio Optimization
Asset Pricing Models
Fixed Income Securities
Estimating the Term Structure of Interest Rates
Derivatives Pricing
Credit Risk Management
Extreme Value Theory
References
Index

## Beta estimation

The sensitivity of a security towards a factor can be estimated from past price movements. We will estimate the beta from the one-factor index model. First, we show the process of collecting and synchronizing data from different sources and then present the simple beta estimation method and, at last, a linear regression model is built.

### Data selection

We download the time series of the price of a given stock, for example Google, and the time series of the price of the market index, the S&P 500, from June 1st 2009 to June 1st 2013 from Quandl, as discussed in the second chapter:

```> library(Quandl)
> Quandl.auth("yourauthenticationtoken")
> G <- Quandl('GOOG/NASDAQ_GOOG',
+   start_date = '2009-06-01', end_date = '2013-06-01')
```

The resulting `G` is a variable containing 6 variables, from which we only need the `Close` values:

```> str(G)
'data.frame':     1018 obs. of  6 variables:
\$ Date  : Date, format: "2009-06-01" "2009-06-02" ...
\$ Open  : num  419 426 426 435...```