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  • Book Overview & Buying Learning Quantitative Finance with R
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Learning Quantitative Finance with R

Learning Quantitative Finance with R

By : Jeet, PRASHANT VATS
3.7 (3)
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Learning Quantitative Finance with R

Learning Quantitative Finance with R

3.7 (3)
By: 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 (10 chapters)
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EGARCH


EGARCH stands for exponential GARCH. EGARCH is an improved form of GARCH and models some of the market scenarios better.

For example, negative shocks (events, news, and so on) tend to impact volatility more than positive shocks.

This model differs from the traditional GARCH in structure due to the log of variance.

Let us take an example to show how to execute EGARCH in R. First define spec for EGARCH and estimate the coefficients, which can be done by executing the following code on the snp data:

> snp <- read.zoo("DataChap4SP500.csv",header = TRUE, sep = ",",format="%m/%d/%Y") 
> egarchsnp.spec = ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(1,1)), 
+                        mean.model=list(armaOrder=c(0,0))) 
> egarchsnp.fit = ugarchfit(egarchsnp.spec, snp$Return) 
> egarchsnp.fit 
> coef(egarchsnp.fit) 

This gives the coefficients as follows:

Figure 4.19: Parameter estimates of EGARCH

Now let us try to forecast, which can be done by executing the following...

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Learning Quantitative Finance with R
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