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Physics Informed Neural Network for Option Pricing

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  • Ashish Dhiman
  • Yibei Hu

Abstract

We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks. Our model is able to accurately capture the price behaviour on simulation data, while also exhibiting reasonable performance for market data. We also experiment with the architecture and learning process of our PINN model to provide more understanding of convergence and stability issues that impact performance.

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  • Ashish Dhiman & Yibei Hu, 2023. "Physics Informed Neural Network for Option Pricing," Papers 2312.06711, arXiv.org.
    • Handle: RePEc:arx:papers:2312.06711
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    References listed on IDEAS

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    1. Damien Lamberton, 2018. "On the binomial approximation of the American put," Papers 1802.05614, arXiv.org, revised Dec 2018.
    2. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    3. Justin Sirignano & Konstantinos Spiliopoulos, 2017. "DGM: A deep learning algorithm for solving partial differential equations," Papers 1708.07469, arXiv.org, revised Sep 2018.
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