European option pricing in regime switching framework via physics-informed residual learning

• Published in: Expert systems with applications Vol. 288; p. 128226
• Main Authors: Pande, Naman Krishna, Pasricha, Puneet, Kumar, Arun, Kumar Gupta, Arvind
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2025
• Subjects: Black-scholes; European options; Heston model; Physics informed deep learning; Regime-switching; Physics informed deep learning; European options; Black-scholes; Regime-switching; Heston model
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2025.128226

•Pricing European options in a regime-switching framework.•Employ physics-informed residual learning, an effective alternative approach•We show that PIRLs become almost immediate once trained, hence no need of retraining.•Our work offers initial results that could help improve derivative pricing strategies. 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 PIRL eliminates the need for retraining and becomes nearly instantaneous once trained, thus offering an efficient and flexible tool for pricing options across a broad range of specifications and parameters.