Corpus ID: 211990146

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

@article{Saporito2020PDGMAN,
  title={PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations},
  author={Yuri F. Saporito and Zhaoyu Zhang},
  journal={arXiv: Computational Finance},
  year={2020}
}
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… Expand
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