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

Antonis Papapantoleon and Jasper Rou

Quantitative Finance, 2025, vol. 25, issue 12, 2009-2020

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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:25:y:2025:i:12:p:2009-2020

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DOI: 10.1080/14697688.2025.2572318

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