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Financial option valuation by unsupervised learning with artificial neural networks

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  • Beatriz Salvador
  • Cornelis W. Oosterlee
  • Remco van der Meer

Abstract

Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied. Instead of using numerical techniques based on finite element or difference methods, we address the problem using ANNs in the context of unsupervised learning. As a result, the ANN learns the option values for all possible underlying stock values at future time points, based on the minimization of a suitable loss function. For the European option, we solve the linear Black-Scholes equation, whereas for the American option, we solve the linear complementarity problem formulation. Two-asset exotic option values are also computed, since ANNs enable the accurate valuation of high-dimensional options. The resulting errors of the ANN approach are assessed by comparing to the analytic option values or to numerical reference solutions (for American options, computed by finite elements).

Suggested Citation

  • Beatriz Salvador & Cornelis W. Oosterlee & Remco van der Meer, 2020. "Financial option valuation by unsupervised learning with artificial neural networks," Papers 2005.12059, arXiv.org.
    • Handle: RePEc:arx:papers:2005.12059
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    References listed on IDEAS

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    1. Henrik Amilon, 2003. "A neural network versus Black-Scholes: a comparison of pricing and hedging performances," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 317-335.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
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    5. Arregui, Iñigo & Salvador, Beatriz & Vázquez, Carlos, 2017. "PDE models and numerical methods for total value adjustment in European and American options with counterparty risk," Applied Mathematics and Computation, Elsevier, vol. 308(C), pages 31-53.
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    7. Sebastian Becker & Patrick Cheridito & Arnulf Jentzen, 2019. "Pricing and hedging American-style options with deep learning," Papers 1912.11060, arXiv.org, revised Jul 2020.
    8. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
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