Neural Network Pricing of American Put Options

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ID: 110915
2020
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Abstract
In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models—a simple one and a more complex one—and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for four large U.S. companies—Procter and Gamble Company (PG), Coca-Cola Company (KO), General Motors (GM), and Bank of America Corp (BAC). Our dataset is composed of all options traded within the period December 2018 until March 2019. Although on average, both NN models perform better than LSM, the simpler model (NN Model 1) performs quite close to LSM. Moreover, the second NN model substantially outperforms the other models, having an RMSE ca. 40% lower than the presented by LSM. The lower RMSE is consistent across all companies, strike levels, and maturities. In summary, all methods present a good accuracy; however, after calibration, NNs produce better results in terms of both execution time and Root Mean Squared Error (RMSE).
Reference Key
gaspar2020risksneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Raquel M. Gaspar;Sara D. Lopes;Bernardo Sequeira;Gaspar, Raquel M.;Lopes, Sara D.;Sequeira, Bernardo;
Journal risks
Year 2020
DOI
10.3390/risks8030073
URL
https://doi.org/10.3390/risks8030073
Keywords
Machine learning neural networks american put options least-squares monte carlo Machine learning neural networks american put options least-squares monte carlo

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