Introduction to R for Quantitative Finance

Gergely Daroczi, Michael Puhle, Edina Berlinger, Peter Csoka, Daniel Havran, Marton Michaletzky, Zsolt Tulassay, Kata Varadi, Agnes Vidovics-Dancs

• Verlag: Packt Publishing
• Jahr: 2013
• ISBN:
  9781783280933
• Auflage: 1
• Thema: Informatica
• Thema: Informatica generale

In Detail

Quantitative finance is an increasingly important area for businesses, and skilled professionals are highly sought after. The statistical computing language R is becoming established in universities and in industry as the lingua franca of data analysis and statistical computing.

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative finance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to financial networks. Each chapter briefly 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 real-world 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 like credit risk management. The last chapters of this book will also provide you with an overview of exciting topics like extreme values and network analysis in quantitative finance.

Approach

This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.

Who this book is for

If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

Introduction to R for Quantitative Finance

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Table of Contents

Introduction to R for Quantitative Finance

Credits

About the Authors

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Time Series Analysis

Working with time series data

Linear time series modeling and forecasting

Modeling and forecasting UK house prices

Model identification and estimation

Model diagnostic checking

Forecasting

Cointegration

Cross hedging jet fuel

Modeling volatility

Volatility forecasting for risk management

Testing for ARCH effects

GARCH model specification

GARCH model estimation

Backtesting the risk model

Forecasting

Summary

2. Portfolio Optimization

Mean-Variance model

Solution concepts

Theorem (Lagrange)

Working with real data

Tangency portfolio and Capital Market Line

Noise in the covariance matrix

When variance is not enough

Summary

3. Asset Pricing Models

Capital Asset Pricing Model

Arbitrage Pricing Theory

Beta estimation

Data selection

Simple beta estimation

Beta estimation from linear regression

Model testing

Data collection

Modeling the SCL

Testing the explanatory power of the individual variance

Summary

4. Fixed Income Securities

Measuring market risk of fixed income securities

Example – implementation in R

Immunization of fixed income portfolios

Net worth immunization

Target date immunization

Dedication

Pricing a convertible bond

Summary

5. Estimating the Term Structure of Interest Rates

The term structure of interest rates and related functions

The estimation problem

Estimation of the term structure by linear regression

Cubic spline regression

Applied R functions

Summary

6. Derivatives Pricing

The Black-Scholes model

The Cox-Ross-Rubinstein model

Connection between the two models

Greeks

Implied volatility

Summary

7. Credit Risk Management

Credit default models

Structural models

Intensity models

Correlated defaults – the portfolio approach

Migration matrices

Getting started with credit scoring in R

Summary

8. Extreme Value Theory

Theoretical overview

Application – modeling insurance claims

Exploratory data analysis

Tail behavior of claims

Determining the threshold

Fitting a GPD distribution to the tails

Quantile estimation using the fitted GPD model

Calculation of expected loss using the fitted GPD model

Summary

9. Financial Networks

Representation, simulation, and visualization of financial networks

Analysis of networks’ structure and detection of topology changes

Contribution to systemic risk – identification of SIFIs

Summary

A. References

Time series analysis

Portfolio optimization

Asset pricing

Fixed income securities

Estimating the term structure of interest rates

Derivatives Pricing

Credit risk management

Extreme value theory

Financial networks

Index

Introduction to R for Quantitative Finance

Copyright © 2013 Packt Publishing

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First published: November 2013

Production Reference: 1151113

Published by Packt Publishing Ltd.

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ISBN 978-1-78328-093-3

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Cover Image by Suresh Mogre (<[email protected]>)

Credits

Authors

Gergely Daróczi

Michael Puhle

Edina Berlinger

Péter Csóka

Dániel Havran

Márton Michaletzky

Zsolt Tulassay

Kata Váradi

Agnes Vidovics-Dancs

Reviewers

Dr. Hari Shanker Gupta

Ronald Hochreiter

Acquisition Editor

Akram Hussain

Lead Technical Editor

Mohammed Fahad

Technical Editors

Venu Manthena

Adrian Raposo

Amit Singh

Copy Editors

Alisha Aranha

Tanvi Gaitonde

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