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

• Verlag: Packt Publishing
• Jahr: 2015
• ISBN:
  9781783552078
• Thema: Computer Science
• Thema: Computer Science General

Mastering R for Quantitative Finance

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Table of Contents

Mastering R for Quantitative Finance

Credits

About the Authors

About the Reviewers

www.PacktPub.com

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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

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First published: March 2015

Production reference: 1030315

Published by Packt Publishing Ltd.

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