A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
提出一种基于深度神经网络的期权定价方法，将偏微分方程转化为能量最小化问题，通过时间步进方式求解，能高效处理粗糙波动率模型的高维问题，数值实验验证了其在提升Heston模型中的准确性和效率。
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.