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Introduction to R for Quantitative Finance

You're reading from   Introduction to R for Quantitative Finance R is a statistical computing language that's ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike.

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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781783280933
Length 164 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (17) Chapters Close

Introduction to R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Time Series Analysis FREE CHAPTER 2. Portfolio Optimization 3. Asset Pricing Models 4. Fixed Income Securities 5. Estimating the Term Structure of Interest Rates 6. Derivatives Pricing 7. Credit Risk Management 8. Extreme Value Theory 9. Financial Networks References Index

When variance is not enough


Variance as a risk measure is convenient, but has some drawbacks. For instance, when using variance, positive changes in the return can be considered as the increase of risk. Therefore, more sophisticated risk measures have been developed.

For example, see the following short demo about various methods applied against the previously described IT_return assets for a quick overview about the options provided by the fPortfolio package:

> Spec <- portfolioSpec()
> setSolver(Spec) <- "solveRshortExact"
> setTargetReturn(Spec) <- mean(colMeans(IT_return))
> efficientPortfolio(IT_return, Spec, 'Short')
> minvariancePortfolio(IT_return, Spec, 'Short')
> minriskPortfolio(IT_return, Spec)
> maxreturnPortfolio(IT_return, Spec)

These R expressions return different portfolio weights computed by various methods not discussed in this introductory chapter. Please refer to the package bundled documentation, such as ?portfolio, and the relevant articles...

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