Mastering R for Quantitative Finance

Use R to optimize your trading strategy and build up your own risk management system

Mastering R for Quantitative Finance

This ebook is included in a Mapt subscription
Edina Berlinger et al.

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Use R to optimize your trading strategy and build up your own risk management system
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Book Details

ISBN 139781783552078
Paperback362 pages

Book Description

R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Its strength lies in data analysis, graphics, visualization, and data manipulation. R is becoming a widely used modeling tool in science, engineering, and business.

The book is organized as a step-by-step practical guide to using R. Starting with time series analysis, you will also learn how to forecast the volume for VWAP Trading. Among other topics, the book covers FX derivatives, interest rate derivatives, and optimal hedging. The last chapters provide an overview on liquidity risk management, risk measures, and more.

The book pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the book, you will be well versed with various financial techniques using R and will be able to place good bets while making financial decisions.

Table of Contents

Chapter 1: Time Series Analysis
Multivariate time series analysis
Volatility modeling
Summary
References and reading list
Chapter 2: Factor Models
Arbitrage pricing theory
Modeling in R
Summary
References
Chapter 3: Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
Summary
References
Chapter 4: Big Data – Advanced Analytics
Getting data from open sources
Introduction to big data analysis in R
K-means clustering on big data
Big data linear regression analysis
Summary
References
Chapter 5: FX Derivatives
Terminology and notations
Currency options
Exchange options
Quanto options
Summary
References
Chapter 6: Interest Rate Derivatives and Models
The Black model
The Vasicek model
The Cox-Ingersoll-Ross model
Parameter estimation of interest rate models
Using the SMFI5 package
Summary
References
Chapter 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
Chapter 8: Optimal Hedging
Hedging of derivatives
Hedging in the presence of transaction costs
Further extensions
Summary
References
Chapter 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
Chapter 10: Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
Neural networks
Logoptimal portfolios
Summary
References
Chapter 11: Asset and Liability Management
Data preparation
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
Summary
References
Chapter 12: Capital Adequacy
Principles of the Basel Accords
Risk measures
Risk categories
Summary
References
Chapter 13: Systemic Risks
Systemic risk in a nutshell
The dataset used in our examples
Core-periphery decomposition
The simulation method
Possible interpretations and suggestions
Summary
References

What You Will Learn

  • Analyze high frequency financial data
  • Build, calibrate, test, and implement theoretical models such as cointegration, VAR, GARCH, APT, Black-Scholes, Margrabe, logoptimal portfolios, core-periphery, and contagion
  • Solve practical, real-world financial problems in R related to big data, discrete hedging, transaction costs, and more.
  • Discover simulation techniques and apply them to situations where analytical formulas are not available
  • Create a winning arbitrage, speculation, or hedging strategy customized to your risk preferences
  • Understand relationships between market factors and their impact on your portfolio
  • Assess the trade-off between accuracy and the cost of your trading strategy

Authors

Table of Contents

Chapter 1: Time Series Analysis
Multivariate time series analysis
Volatility modeling
Summary
References and reading list
Chapter 2: Factor Models
Arbitrage pricing theory
Modeling in R
Summary
References
Chapter 3: Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
Summary
References
Chapter 4: Big Data – Advanced Analytics
Getting data from open sources
Introduction to big data analysis in R
K-means clustering on big data
Big data linear regression analysis
Summary
References
Chapter 5: FX Derivatives
Terminology and notations
Currency options
Exchange options
Quanto options
Summary
References
Chapter 6: Interest Rate Derivatives and Models
The Black model
The Vasicek model
The Cox-Ingersoll-Ross model
Parameter estimation of interest rate models
Using the SMFI5 package
Summary
References
Chapter 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
Chapter 8: Optimal Hedging
Hedging of derivatives
Hedging in the presence of transaction costs
Further extensions
Summary
References
Chapter 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
Chapter 10: Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
Neural networks
Logoptimal portfolios
Summary
References
Chapter 11: Asset and Liability Management
Data preparation
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
Summary
References
Chapter 12: Capital Adequacy
Principles of the Basel Accords
Risk measures
Risk categories
Summary
References
Chapter 13: Systemic Risks
Systemic risk in a nutshell
The dataset used in our examples
Core-periphery decomposition
The simulation method
Possible interpretations and suggestions
Summary
References

Book Details

ISBN 139781783552078
Paperback362 pages
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