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

Multicollinearity


If the predictor variables are correlated then we need to detect multicollinearity and treat it. Recognition of multicollinearity is crucial because two or more variables are correlated, which shows a strong dependence structure between those variables, and we are using correlated variables as independent variables, which end up having a double effect of these variables on the prediction because of the relation between them. If we treat the multicollinearity and consider only variables which are not correlated then we can avoid the problem of double impact.

We can find multicollinearity by executing the following code:

> vif(MultipleR.lm) 

This gives the multicollinearity table for the predictor variables:

Figure 3.8: VIF table for multiple regression model

Depending upon the values of VIF, we can drop the irrelevant variable.