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

VGARCH


VGARCH stands for vector GARCH or multivariate GARCH. In the financial domain, the assumption is that financial volatilities move together over time across assets and markets. Acknowledging this aspect through a multivariate modeling framework leads to a better model separate univariate model. It helps in making better decision tools in various areas, such as asset pricing, portfolio selection, option pricing, and hedging and risk management. There are multiple options in R for building in multivariate mode.

Let us consider an example of multivariate GARCH in R for the last year of data from the S&P500 and DJI index:

>install.packages("rmgarch")
>install.packages("PerformanceAnalytics")
>library(rmgarch)
>library(PerformanceAnalytics)
>snpdji <- read.zoo("DataChap4SPDJIRet.csv",header = TRUE, sep = ",",format="%m/%d/%Y")
>garch_spec = ugarchspec(mean.model = list(armaOrder = c(2,1)),variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model...