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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

Chapter 8. Extreme Value Theory

The risk of extreme losses is at the heart of many risk management problems both in insurance and finance. An extreme market move might represent a significant downside risk to the security portfolio of an investor. Reserves against future credit losses need to be sized to cover extreme loss scenarios in a loan portfolio. The required level of capital for a bank should be high enough to absorb extreme operational losses. Insurance companies need to be prepared for losses arising from natural or man-made catastrophes, even of a magnitude not experienced before.

Extreme Value Theory (EVT) is concerned with the statistical analysis of extreme events. The methodology provides distributions that are consistent with extreme observations and, at the same time, have parametric forms that are supported by theory. EVT's theoretical considerations compensate the unreliability of traditional estimates (caused by sparse data on extremes). EVT allows the quantification of...

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