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arXiv · 2403.00746

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models

Antonis Papapantoleon·Jasper Rou
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Abstract

We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness, and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples, with particular focus in the lifted Heston model.

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BibTeXRIS

Antonis Papapantoleon, Jasper Rou. 2024-03-01. A time-stepping deep gradient flow method for option pricing in (rough) diffusion models. https://arxiv.org/abs/2403.00746

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