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Mastering R for Quantitative Finance
Edina Berlinger, Ferenc Illes, Milan Badics, Adam Banai, Gergely Daroczi, Barbara Domotor, Gergely Gabler, Daniel Havran, Peter Juhasz, Istvan Margitai
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eBook - ePub
Mastering R for Quantitative Finance
Edina Berlinger, Ferenc Illes, Milan Badics, Adam Banai, Gergely Daroczi, Barbara Domotor, Gergely Gabler, Daniel Havran, Peter Juhasz, Istvan Margitai
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Information
Mastering R for Quantitative Finance
Table of Contents
Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
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
Multivariate time series analysis
Cointegration
Vector autoregressive models
VAR implementation example
Cointegrated VAR and VECM
Volatility modeling
GARCH modeling with the rugarch package
The standard GARCH model
The Exponential GARCH model (EGARCH)
The Threshold GARCH model (TGARCH)
Simulation and forecasting
Summary
References and reading list
2. Factor Models
Arbitrage pricing theory
Implementation of APT
Fama-French three-factor model
Modeling in R
Data selection
Estimation of APT with principal component analysis
Estimation of the Fama-French model
Summary
References
3. Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
The data
Loading the data
The seasonal component
AR(1) estimation and forecasting
SETAR estimation and forecasting
Interpreting the results
Summary
References
4. Big Data – Advanced Analytics
Getting data from open sources
Introduction to big data analysis in R
K-means clustering on big data
Loading big matrices
Big data K-means clustering analysis
Big data linear regression analysis
Loading big data
Fitting a linear regression model on large datasets
Summary
References
5. FX Derivatives
Terminology and notations
Currency options
Exchange options
Two-dimensional Wiener processes
The Margrabe formula
Application in R
Quanto options
Pricing formula for a call quanto
Pricing a call quanto in R
Summary
References
6. Interest Rate Derivatives and Models
The Black model
Pricing a cap with Black's model
The Vasicek model
The Cox-Ingersoll-Ross model
Parameter estimation of interest rate models
Using the SMFI5 package
Summary
References
7. Exotic Options
A general pricing approach
The role of dynamic hedging
How R can help a lot
A glance beyond vanillas
Greeks – the link back to the vanilla world
Pricing the Double-no-touch option
Another way to price the Double-no-touch option
The life of a Double-no-touch option – a simulation
Exotic options embedded in structured products
Summary
References
8. Optimal Hedging
Hedging of derivatives
Market risk of derivatives
Static delta hedge
Dynamic delta hedge
Comparing the performance of delta hedging
Hedging in the presence of transaction costs
Optimization of the hedge
Optimal hedging in the case of absolute transaction costs
Optimal hedging in the case of relative transaction costs
Further extensions
Summary
References
9. Fundamental Analysis
The basics of fundamental analysis
Collecting data
Revealing connections
Including multiple variables
Separating investment targets
Setting classification rules
Backtesting
Industry-specific investment
Summary
References
10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
The TA toolkit
Markets
Plotting charts - bitcoin
Built-in indicators
SMA and EMA
RSI
MACD
Candle patterns: key reversal
Evaluating the signals and managing the position
A word on money management
Wraping up
Neural networks
Forecasting bitcoin prices
Evaluation of the strategy
Logoptimal portfolios
A universally consistent, non-parametric investment strategy
Evaluation of the strategy
Summary
References
11. Asset and Liability Management
Data preparation
Data source at first glance
Cash-flow generator functions
Preparing the cash-flow
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
A Model of deposit interest rate development
Static replication of non-maturity deposits
Summary
References
12. Capital Adequacy
Principles of the Basel Accords
Basel I
Basel II
Minimum capital requirements
Supervisory review
Transparency
Basel III
Risk measures
Analytical VaR
Historical VaR
Monte-Carlo simulation
Risk categories
Market risk
Credit risk
Operational risk
Summary
References
13. Systemic Risks
Systemic risk in a nutshell
The dataset used in our examples
Core-periphery decomposition
Implementation in R
Results
The simulation method
The simulation
Implementation in R
Results
Possible interpretations and suggestions
Summary
References
Index
Mastering R for Quantitative Finance
Copyright © 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
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First published: March 2015
Production reference: 1030315
Published by Packt Publishing Ltd.
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