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Bibliographic Details
Main Authors:	Gnoatto, Alessandro, Oberpriller, Katharina, Picarelli, Athena
Format:	Preprint
Published:	2025
Subjects:	Probability Numerical Analysis Optimization and Control Computational Finance Pricing of Securities 60H35, 65C30, 65N75
Online Access:	https://arxiv.org/abs/2501.09727
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Table of Contents:

We study the error arising in the numerical approximation of FBSDEs and related PIDEs by means of a deep learning-based method. Our results focus on decoupled FBSDEs with jumps and extend the seminal work of HAn and Long (2020) analyzing the numerical error of the deep BSDE solver proposed in E et al. (2017). We provide a priori and a posteriori error estimates for the finite and infinite activity case.