Towards Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations

Abstract

Applications in quantitative finance such as optimal trade execution, risk management of options, and optimal asset allocation involve the solution of high dimensional and nonlinear Partial Differential Equations (PDEs). The connection between PDEs and systems of Forward-Backward Stochastic Differential Equations (FBSDEs) enables the use of advanced simulation techniques to be applied even in the high dimensional setting. Unfortunately, when the underlying application contains nonlinear terms, then classical methods both for simulation and numerical methods for PDEs suffer from the curse of dimensionality. Inspired by the success of deep learning, several researchers have recently proposed to address the solution of FBSDEs using deep learning. We discuss the dynamical systems point of view of deep learning and compare several architectures in terms of stability, generalization, and robustness. In order to speed up the computations, we propose to use a multilevel discretization technique. Our preliminary results suggest that the multilevel discretization method improves solutions times by an order of magnitude compared to existing methods without sacrificing stability or robustness.

Accepted at NeurIPS 2019 Workshops

References in corpus (6)

• Solving high-dimensional partial differential equations using deep learning
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
• Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective
• Adversarial Examples - A Complete Characterisation of the Phenomenon
• IMEXnet: A Forward Stable Deep Neural Network

Cited by in corpus (5)

• An overview on deep learning-based approximation methods for partial differential equations
• Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations
• Continuous-in-Depth Neural Networks
• Learning the temporal evolution of multivariate densities via normalizing flows
• Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations