Neural Network pricing of American put options

Raquel Gaspar, Sara D. Lopes and Bernardo Sequeira

No 2020/0122, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

Abstract: In this paper we use neural networks (NN), a machine learning method, to price Americanput options. We propose two distinct NN models – a simple one and a more complex one. The performance of two NN models is compared to the popular Least-Square Monte Carlo Method(LSM).This study relies on market American put option prices, with four large US companies asunderlying – Bank of America Corp (BAC), General Motors (GM), Coca-Cola Company (KO) andProcter and Gamble Company (PG). Our dataset includes all options traded from December 2018to March 2019.All methods show a good accuracy, however, once calibrated, NNs do better in terms ofexecution time and Root Mean Square Error (RMSE). Although on average both NN modelsperform better than LSM, the simpler model (NN model 1) performs quite close to LSM. On the other hand our NN model 2 substantially outperforms the other models, having a RMSE ca. 40% lower than that of the LSM. The lower RMSE is consistent across all companies, strike levels andmaturities.

Keywords: Machine learning; Neural networks; American put options; Least-square Monte Carlo (search for similar items in EconPapers)
JEL-codes: C45 C63 G13 G17 (search for similar items in EconPapers)
Date: 2020-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Neural Network Pricing of American Put Options (2020)
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