#### Overview of this book

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.
Introduction to R for Quantitative Finance
Credits
www.PacktPub.com
Preface
Free Chapter
Time Series Analysis
Portfolio Optimization
Asset Pricing Models
Fixed Income Securities
Estimating the Term Structure of Interest Rates
Derivatives Pricing
Credit Risk Management
Extreme Value Theory
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...