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Python for Finance Cookbook - Second Edition

You're reading from  Python for Finance Cookbook - Second Edition

Product type Book
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Pages 740 pages
Edition 2nd Edition
Languages
Author (1):
Eryk Lewinson Eryk Lewinson
Profile icon Eryk Lewinson

Table of Contents (18) Chapters

Preface Acquiring Financial Data Data Preprocessing Visualizing Financial Time Series Exploring Financial Time Series Data Technical Analysis and Building Interactive Dashboards Time Series Analysis and Forecasting Machine Learning-Based Approaches to Time Series Forecasting Multi-Factor Models Modeling Volatility with GARCH Class Models Monte Carlo Simulations in Finance Asset Allocation Backtesting Trading Strategies Applied Machine Learning: Identifying Credit Default Advanced Concepts for Machine Learning Projects Deep Learning in Finance Other Books You May Enjoy
Index

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 spaced intervals and the early exercise can happen only at those particular time steps. Effectively...

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