#### 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

## Applied R functions

Although we have already used some functions from the `termstrc` package in the previous example to demonstrate how one can determine the ideal number of knot points and also specify those, this process can be done in an easier manner with some further R functions, as shown in the following command lines:

```> x <- estim_cs(govbonds, 'GERMANY')
> x\$knotpoints[[1]]
DE0001135101 DE0001141463 DE0001135218 DE0001135317
0.0000     1.006027     2.380274     5.033425     9.234521 31.44657
```

First we used the `estim_cs` function that estimates the term structure of coupon bonds based on cubic splines (Ferstl-Haydn, 2010) and returns the knot points in a list with the `knotpoints` name. Please note that `estim_cs` works with a list—just like most functions in the package—that's why `x\$knotpoints` returned a list from which we checked only the first element that was identical to the values we computed manually in the preceding section.

There are a bunch of other useful...