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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis FREE CHAPTER 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Introduction to big data analysis in R

Big data refers to the situations when volume, velocity, or a variety of data exceeds the abilities of our computation capacity to process, store, and analyze them. Big data analysis has to deal not only with large datasets but also with computationally intensive analyses, simulations, and models with many parameters.

Leveraging large data samples can provide significant advantages in the field of quantitative finance; we can relax the assumption of linearity and normality, generate better perdition models, or identify low-frequency events.

However, the analysis of large datasets raises two challenges. First, most of the tools of quantitative analysis have limited capacity to handle massive data, and even simple calculations and data-management tasks can be challenging to perform. Second, even without the capacity limit, computation on large datasets may be extremely time consuming.

Although R is a powerful and robust program with a rich set of statistical...

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