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Neural Network Pricing of American Put Options

Author

Listed:
  • Raquel M. Gaspar

    (ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-078 Lisbon, Portugal
    CEMAPRE/REM Research Center, Rua do Quelhas 6, 1200-078 Lisbon, Portugal)

  • Sara D. Lopes

    (ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-078 Lisbon, Portugal
    CEMAPRE/REM Research Center, Rua do Quelhas 6, 1200-078 Lisbon, Portugal)

  • Bernardo Sequeira

    (ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-078 Lisbon, Portugal)

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).

Suggested Citation

  • Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020. "Neural Network Pricing of American Put Options," Risks, MDPI, vol. 8(3), pages 1-24, July.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:3:p:73-:d:379508
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    Cited by:

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    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-squares 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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