Book Image

Introduction to R for Quantitative Finance

By : Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, Kata Váradi, Agnes Vidovics-Dancs, Agnes Vidovics Dancs, Michael Phule, Zsolt Tulassay, Peter Csoka, Marton Michaletzky, Edina Berlinger (EURO), Varadi Kata
Book Image

Introduction to R for Quantitative Finance

By: Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, Kata Váradi, Agnes Vidovics-Dancs, Agnes Vidovics Dancs, Michael Phule, Zsolt Tulassay, Peter Csoka, Marton Michaletzky, Edina Berlinger (EURO), Varadi Kata

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.
Table of Contents (17 chapters)
Introduction to R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 7. Credit Risk Management

This chapter introduces some useful tools for credit risk management. Credit risk is the distribution of the financial losses due to unexpected changes in the credit quality of a counterparty in a financial agreement (Giesecke 2004). Several tools and industrial solutions were developed for managing credit risk. In accordance with the literature, one may consider credit risk as the default risk, downgrade risk, or counterparty risk. In most cases, the default risk is related directly to the risk of non-performance of a claim or credit. In contrast, downgrade risk arises when the price of a bond declines due to its worsening credit rating without any realized credit event. Counterparty risk means the risk when the counterparty of a contract does not meet the contractual obligations. However, the contractual or regulatory definition of a credit event can usually be wider than just a missed payment. The modeling end estimation of the possibility of default...