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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
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Dr. Param Jeet
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R FREE CHAPTER 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

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...

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