2025/11/11 by Molla, Hasib Uddin, Backhouse, Matthew, Banarjee, Ankit +1
#FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Mathematical Finance (q-fin.MF)
paper · doi:10.48550/arxiv.2511.08735
In this work, we extend deep learning-based numerical methods to fully coupled forward-backward stochastic differential equations (FBSDEs) within a non-Markovian framework. Error estimates and convergence are provided. In contrast to the existing literature, our approach not only analyzes the non-Markovian framework but also addresses fully coupled settings, in which both the drift and diffusion coefficients of the forward process may be random and depend on the backward components Y and Z. Furthermore, we illustrate the practical applicability of our framework by addressing utility maximization problems under rough volatility, which are solved numerically with the proposed deep learning-based methods.