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European Option Pricing in Regime Switching Framework via Physics-Informed Residual Learning

Author

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  • Naman Krishna Pande
  • Puneet Pasricha
  • Arun Kumar
  • Arvind Kumar Gupta

Abstract

In this article, we employ physics-informed residual learning (PIRL) and propose a pricing method for European options under a regime-switching framework, where closed-form solutions are not available. We demonstrate that the proposed approach serves an efficient alternative to competing pricing techniques for regime-switching models in the literature. Specifically, we demonstrate that PIRLs eliminate the need for retraining and become nearly instantaneous once trained, thus, offering an efficient and flexible tool for pricing options across a broad range of specifications and parameters.

Suggested Citation

  • Naman Krishna Pande & Puneet Pasricha & Arun Kumar & Arvind Kumar Gupta, 2024. "European Option Pricing in Regime Switching Framework via Physics-Informed Residual Learning," Papers 2410.10474, arXiv.org.
    • Handle: RePEc:arx:papers:2410.10474
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    References listed on IDEAS

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    1. Damir Filipović & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Swiss Finance Institute Research Paper Series 22-95, Swiss Finance Institute.
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    7. Donatien Hainaut & Alex Casas, 2024. "Option pricing in the Heston model with physics inspired neural networks," Annals of Finance, Springer, vol. 20(3), pages 353-376, September.
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