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 9. Financial Networks

We have seen in the previous chapter how extreme events coming from asymmetric and fat-tailed distributions can be modeled and how the risk associated with extreme events can be measured and managed.

In some cases we have access to financial data that enables us to construct complex networks. In financial networks, it is quite usual that the distribution of some attributes (degree, quantity, and so on) is highly asymmetric and fat-tailed too.

By nature, available financial networks are usually not complete; they do not contain either all possible players, or all possible connections, or all relevant attributes. But even in their limited state, they constitute an extremely rich and informative data set which can help us to get insight into the detailed microstructure of the market under investigation.

This chapter gives an overview of how financial networks can be represented, simulated, visualized, and analyzed in R. We will focus on two important practical problems...