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Learning Quantitative Finance with R

You're reading from  Learning Quantitative Finance with R

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Pages 284 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Param Jeet Dr. Param Jeet
Profile icon Dr. Param Jeet
PRASHANT VATS PRASHANT VATS
Profile icon PRASHANT VATS
View More author details

Table of Contents (16) Chapters

Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to R 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

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.

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