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Neural Network pricing of American put options

Author

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  • Raquel M. Gaspar
  • Sara D. Lopes
  • Bernardo Sequeira

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.

Suggested Citation

  • Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020. "Neural Network pricing of American put options," Working Papers REM 2020/0122, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp01222020
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    References listed on IDEAS

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    Cited by:

    1. Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
    2. S'andor Kuns'agi-M'at'e & G'abor F'ath & Istv'an Csabai & G'abor Moln'ar-S'aska, 2022. "Deep Weighted Monte Carlo: A hybrid option pricing framework using neural networks," Papers 2208.14038, arXiv.org, revised Dec 2022.
    3. Antal Ratku & Dirk Neumann, 2022. "Derivatives of feed-forward neural networks and their application in real-time market risk management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 947-965, September.
    4. Riccardo Aiolfi & Nicola Moreni & Marco Bianchetti & Marco Scaringi & Filippo Fogliani, 2021. "Learning Bermudans," Papers 2105.00655, arXiv.org.

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    More about this item

    Keywords

    Machine learning; Neural networks; American put options; Least-square Monte Carlo;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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