#### 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

## Correlated defaults – the portfolio approach

In this section, we show you how to deal with correlated random variables with copulas for the simulation of loss distributions of credit portfolios. The copula function is a joint cumulative distribution function of uniform distributed random variables. The copula function contains all the information on the dependence structure of the components. Any of the continuously distributed random variables can be transformed into uniformly distributed variables, which allows for the possibility of general modeling; for example, it can be combined with the structural approach. Using the copula package, we demonstrate how to simulate two uniformly distributed random variables with Gaussian and t-copulas, and how to fit in a Gaussian copula parameter from the generated data. (One can apply this method for historical datasets also.) This package also serves useful functions in a wide range of topics about copulas, such as plotting or fitting copula classes...