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

VaR


Value at risk is a measure in risk management to measure the potential risk which can occur to the portfolio of an investor. VaR imputed at 5% confidence means that the loss will not be less than predicted value 95% of the time or, in other words, loss will be greater in 5% of times than predicted value.

There are three common ways of computing value at risk:

  • Parametric VaR

  • Historical VaR

  • Monte Carlo VaR

In this section, we will be capturing the first two, and the third one will be captured in the next section.

Parametric VaR

Parametric VaR is also known as the variance-covariance method and is used to find VaR using mean and standard deviation as parameters.

qnorm is used for value at risk calculation using parametric methods. It uses the parameters mean and standard deviation. The general syntax is as follows:

qnorm(p,mean,sd) 

Here, p is the desired percentile; mean is the given mean of the sample; and sd is the standard deviation of the sample.

Let us assume that the average return of a stock...