Numerical approximation of RBSDEs via regularization

Ankush Agarwal (), Emmanuel Gobet () and Yihan Zou ()
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Ankush Agarwal: Western University, London, Canada
Emmanuel Gobet: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
Yihan Zou: University of Glasgow

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Abstract: In this paper, we design a numerical scheme based on a regularization approach to approximate the solution of Reflected Backward Stochastic Differential Equation (RBSDE) and we study its convergence.We establish the order 1 convergence between the continuous regularized solution and the reflected solution, in full generality, as a function of the regularization parameter. The convergence between the continuous regularized solution and the corresponding RBSDE is obtained in both the almost sure and the L p (F)-sense (p ≥ 2). Additionally, we derive the convergence rate for the discretized version of the regularized RBSDE under mild regularity conditions.

Keywords: Reflected backward stochastic differential equations; regularization; error bound (search for similar items in EconPapers)
Date: 2026-06-25
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