PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations

• URL: http://arxiv.org/abs/2003.02035v2
• Date: Fri, 3 Apr 2020 19:18:44 GMT
• Title: PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations
• Authors: Yuri F. Saporito and Zhaoyu Zhang
• Abstract summary: We propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs) The method consists of using a combination of feed-forward and Long-Term Memory architectures to model the solution of the PPDE. We analyze several numerical examples, many from the Financial Mathematics literature, that show the capabilities of the method under very different situations.
• Score: 4.1499725848998965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs). These equations firstly appeared in the seminal work of Dupire [2009], where the functional It\^o calculus was developed to deal with path-dependent financial derivatives contracts. More specificaly, we generalize the Deep Galerking Method (DGM) of Sirignano and Spiliopoulos [2018] to deal with these equations. The method, which we call Path-Dependent DGM (PDGM), consists of using a combination of feed-forward and Long Short-Term Memory architectures to model the solution of the PPDE. We then analyze several numerical examples, many from the Financial Mathematics literature, that show the capabilities of the method under very different situations.

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