A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
<|reference_start|>A time-stepping deep gradient flow method for option pricing in (rough) diffusion models: 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.<|reference_end|>
@article{papapantoleon2024a,
    title={A time-stepping deep gradient flow method for option pricing in (rough)
  diffusion models},
    author={Antonis Papapantoleon and Jasper Rou},
    journal={arXiv preprint arXiv:2403.00746},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00746},
    primaryClass={q-fin.CP cs.LG math.PR q-fin.MF}
}