Pricing

European Option Pricing in Regime Switching Framework via Physics-Informed Residual Learning ArXiv ID: 2410.10474 “View on arXiv” Authors: Unknown 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. ...

$ε$-Policy Gradient for Online Pricing ArXiv ID: 2405.03624 “View on arXiv” Authors: Unknown Abstract Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an $ε$-policy gradient algorithm for the online pricing learning task. The algorithm extends $ε$-greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability $ε$ and the exploitation cost in terms of the gradient descent optimization and gradient estimation errors. The algorithm achieves an expected regret of order $\mathcal{“O”}(\sqrt{“T”})$ (up to a logarithmic factor) over $T$ trials. ...