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

Correlation


Correlation plays a very important role in quant finance. It not only determines the relation between the financial attributes but also plays a crucial role in predicting the future of financial instruments. Correlation is the measure of linear relationship between the two financial attributes. Now let us try to compute the different types of correlation in R using Sampledata, which is used in identifying the orders of components of predictive financial models.

Correlation can be computed by the following code. Let's first subset the data and then run the function for getting correlation:

x<-Sampledata[,2:5] 
rcorr(x, type="pearson") 

This generates the following correlation matrix, which shows the measure of linear relationship between the various daily level prices of a stock:

Open

High

Low

Close

Open

1

0.962062

0.934174

0.878553

High

0.962062

1

0.952676

0.945434

Low

0.934174

0.952676

1

0.960428

Close

0.878553

0.945434

0.960428...