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Mastering R for Quantitative Finance

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Mastering R for Quantitative Finance

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Mastering R for Quantitative Finance
Mar, 2015 | 362 pages
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Table of Contents (20 chapters)
Mastering R for Quantitative Finance
Mastering R for Quantitative Finance
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Time Series Analysis
Time Series Analysis
Multivariate time series analysis
Volatility modeling
Summary
References and reading list
2
Factor Models
Factor Models
Arbitrage pricing theory
Modeling in R
Summary
References
3
Forecasting Volume
Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
Summary
References
4
Big Data – Advanced Analytics
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
FX Derivatives
FX Derivatives
Terminology and notations
Currency options
Exchange options
Quanto options
Summary
References
6
Interest Rate Derivatives and Models
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
Exotic Options
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
Optimal Hedging
Hedging of derivatives
Hedging in the presence of transaction costs
Further extensions
Summary
References
9
Fundamental Analysis
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
Technical Analysis, Neural Networks, and Logoptimal Portfolios
Market efficiency
Technical analysis
Neural networks
Logoptimal portfolios
Summary
References
11
Asset and Liability Management
Asset and Liability Management
Data preparation
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
Summary
References
12
Capital Adequacy
Capital Adequacy
Principles of the Basel Accords
Risk measures
Risk categories
Summary
References
13
Systemic Risks
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
Index
Index

References

  • Adler, D., Nenadic, O., Zucchini, W.,Gläser, C. (2007): The ff package: Handling Large Data Sets in R with Memory Mapped Pages of Binary Flat Files

  • Enea, M. (2009): Fitting Linear Models and Generalized Linear Models with large data sets in R. In book of short papers, conference on "Statistical Methods for the analysis of large data-sets", Italian Statistical Society, Chieti-Pescara, 23-25 September 2009, 411-414.

  • Kane, M.,Emerson, JW., Weston (2010): The Bigmemory Project, Yale University

  • Kane, M.,Emerson, JW., Weston, S. (2013): Scalable Strategies for Computing with Massive Data. Journal of Statistical Software , Vol. 55, Issue 14

  • Lumley, T. (2009) biglm: bounded memory linear and generalized linear models. R package version 0.7

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