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

Simple linear regression


In simple linear regression, we try to predict one variable in terms of a second variable called a predictor variable. The variable we are trying to predict is called the dependent variable and is denoted by y, and the independent variable is denoted by x. In simple linear regression, we assume a linear relationship between the dependent attribute and predictor attribute.

First we need to plot the data to understand the linear relationship between the dependent variable and independent variable. Here our, data consists of two variables:

  • YPrice: Dependent variable

  • XPrice: Predictor variable

In this case, we are trying to predict Yprice in terms of XPrice. StockXprice is the independent variable and StockYprice is the dependent variable. For every element of StockXprice, there is an element of StockYprice, which implies one-to-one mapping between elements of StockXprice and StockYprice.

A few lines of data used for the following analysis are displayed using the following...