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Python for Finance Cookbook

You're reading from   Python for Finance Cookbook Over 50 recipes for applying modern Python libraries to financial data analysis

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Length 432 pages
Edition 1st Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (12) Chapters Close

Preface 1. Financial Data and Preprocessing 2. Technical Analysis in Python FREE CHAPTER 3. Time Series Modeling 4. Multi-Factor Models 5. Modeling Volatility with GARCH Class Models 6. Monte Carlo Simulations in Finance 7. Asset Allocation in Python 8. Identifying Credit Default with Machine Learning 9. Advanced Machine Learning Models in Finance 10. Deep Learning in Finance 11. Other Books You May Enjoy

Pricing American options with Least Squares Monte Carlo

In this recipe, we learn how to valuate American options. The key difference between European and American options is that the latter can be exercised at any time before and including the maturity date – basically, whenever the underlying asset's price moves favorably for the option holder.

This behavior introduces additional complexity to the valuation and there is no closed-form solution to this problem. When using Monte-Carlo simulations, we cannot only look at the terminal value on each sample path, as the option's exercise can happen anywhere along the path. That is why we need to employ a more sophisticated approach called Least Squares Monte Carlo (LSMC), which was introduced by Longstaff and Schwartz (2001).

First of all, the time axis spanning [0, T] is discretized into a finite number of equally...

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