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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 2. Statistical Modeling FREE CHAPTER 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

This chapter used derivative pricing only in terms of implementation in R, and various packages such as foptions, termstrc, CreditMetrics, credule, GUIDE, and fExoticOptions to price options, bonds, credit spreads, credit default swaps, and interest rate derivatives, and different types of exotic options were used. Derivative pricing is crucial in derivative trading and it is very important to learn it.

This chapter also covered the Black-Scholes and Cox-Ross-Rubinstein methods, along with Greeks and implied volatility for options. It also explained bond price and yield curves. It also used functions which explain how credit spread, credit default swaps, and interest rate derivatives are priced. Toward the end, it covered various types of exotic options. It used data given in relevant packages and implemented functions.

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