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

arXiv:2403.00746v1 Announce Type: cross
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 …

abstract arxiv cs.lg deep learning differential differential equation diffusion diffusion models energy equation flow gradient math.pr novel pricing q-fin.cp q-fin.mf type