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

Summary


Regression is the backbone of any analysis and the reader cannot go ahead without touching on it. In this chapter, I have presented linear regression and multivariate regression and how they are used for prediction. The R function lm() is used to implement both simple and multivariate linear regression. I also presented significance testing along with residual calculations and the normality plot, which tests residuals for normality using a qq plot. Analysis of variance (ANOVA) is used to select the difference means of two or more samples. Multivariate linear regression involves many variables, and the coefficient of each variable is different, which varies the importance of each variable and is ranked accordingly. Stepwise regression is used to select variables which are important in the regression. Time series analysis does not represent the complete information sometimes. It becomes necessary to explore frequency analysis, which can be done with wavelet, fast Fourier and Hilbert...