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Mastering R for Quantitative Finance

Mastering R for Quantitative Finance

By : Gabler
4 (11)
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Mastering R for Quantitative Finance

Mastering R for Quantitative Finance

4 (11)
By: Gabler

Overview of this book

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.
Table of Contents (15 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Time Series Analysis
1. Time Series Analysis
Multivariate time series analysis
Volatility modeling
Summary
References and reading list
2
2. Factor Models
2. Factor Models
Arbitrage pricing theory
Modeling in R
Summary
References
3
3. Forecasting Volume
3. Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
Summary
References
4
4. Big Data – Advanced Analytics
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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
5
5. FX Derivatives
5. FX Derivatives
Terminology and notations
Currency options
Exchange options
Quanto options
Summary
References
6
6. Interest Rate Derivatives and Models
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
7
7. Exotic Options
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
8. Optimal Hedging
8. Optimal Hedging
Hedging of derivatives
Hedging in the presence of transaction costs
Further extensions
Summary
References
9
9. Fundamental Analysis
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
10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
Neural networks
Logoptimal portfolios
Summary
References
11
11. Asset and Liability Management
11. Asset and Liability Management
Data preparation
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
Summary
References
12
12. Capital Adequacy
12. Capital Adequacy
Principles of the Basel Accords
Risk measures
Risk categories
Summary
References
13
13. Systemic Risks
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
14
Index
Index

K-means clustering on big data

Data frames and matrices are easy-to-use objects in R, with typical manipulations that execute quickly on datasets with a reasonable size. However, problems can arise when the user needs to handle larger data sets. In this section, we will illustrate how the bigmemory and biganalytics packages can solve the problem of too large datasets, which is impossible to handle by data frames or data tables.

Note

The latest updates of bigmemory, biganalytics, and biglm packages are not available on Windows at time of writing this chapter. The examples shown here assume that R Version 2.15.3 is the current state-of-the-art version of R for Windows.

In the following example, we will perform K-means clustering on large datasets. For illustrative purposes, we will use the Airline Origin and Destination Survey data of the U.S. Bureau of Transportation Statistics. The datasets contain the summary characteristics of more than 3 million domestic flights, including the itinerary...

End of Section 4
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