Book Image

Learning Quantitative Finance with R

By : Dr. Param Jeet, PRASHANT VATS
Book Image

Learning Quantitative Finance with R

By: Dr. Param Jeet, PRASHANT VATS

Overview of this book

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
Table of Contents (16 chapters)
Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Credit spread


Credit risk is one of the major problems for financial institutions. The major cause for this is credit quality, and credit spread values help to understand credit risk depending upon the credit quality. Credit spread is an important concept in institutional trading as credit spread depends upon the quality or rating of bonds. It is the difference in bond yield of two bonds with similar maturity but with different bond ratings. We are going to use the CreditMetrics package for this, which can be installed and imported to the R workspace using the following two commands:

> install.packages('CreditMetrics') 
> library('CreditMetrics') 

Credit spread is calculated using cm.cs(), which has just two parameters. The first parameter is the one-year migration matrix for some institution or government which issues credit and the second parameter is loss given default (LGD), which means maximum loss if the obligor of credit defaults. Normally, credit with rating AAA is on the top...