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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
Author Profile Icon Dr. Param Jeet
Dr. Param Jeet
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R FREE CHAPTER 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

Summary

In this chapter, we have discussed various optimization techniques used in trading algorithms and parameter estimation. The covered optimization techniques were dynamic rebalancing, walk forward testing, grid testing, and genetic algorithm.

In the next chapter, the topics covered are using foptions, termstrc, CreditMetrics, credule, GUIDE, and fExoticOptions to price options, bond, credit spreads, credit default swaps, interest rate derivatives, and different types of exotic options.

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