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

TL;DR

This paper introduces a physics-informed residual learning approach for efficiently pricing European options in regime-switching models where traditional solutions are unavailable, offering a fast and flexible alternative to existing methods.

Contribution

The paper presents a novel PIRL-based method for European option pricing in regime-switching models, eliminating retraining needs and enabling rapid, accurate pricing across various parameters.

Findings

PIRL provides near-instantaneous pricing after training.

The method outperforms traditional techniques in efficiency.

It is adaptable to a wide range of model specifications.

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.