A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations

• URL: http://arxiv.org/abs/2209.15010v1
• Date: Wed, 28 Sep 2022 14:34:58 GMT
• Title: A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations
• Authors: Jiang Yu Nguwi and Nicolas Privault
• Abstract summary: PPDE solutions can be approximated by a probabilistic representation, implemented in the literature by the estimation of conditional expectations using regression. We overcome this limitation by the use of deep learning methods, and we show that this setting allows for the derivation of the bounds of error on the approximation of conditional expectations. In comparison with other deep learning approaches, our algorithm appears to be more accurate, especially in large dimensions.
• Score: 1.09650651784511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work on Path-Dependent Partial Differential Equations (PPDEs) has shown that PPDE solutions can be approximated by a probabilistic representation, implemented in the literature by the estimation of conditional expectations using regression. However, a limitation of this approach is to require the selection of a basis in a function space. In this paper, we overcome this limitation by the use of deep learning methods, and we show that this setting allows for the derivation of error bounds on the approximation of conditional expectations. Numerical examples based on a two-person zero-sum game, as well as on Asian and barrier option pricing, are presented. In comparison with other deep learning approaches, our algorithm appears to be more accurate, especially in large dimensions.

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