Convergence and qualitative properties of modified explicit schemes for BSDEs with polynomial growth
Arnaud Lionnet, Gon\c{c}alo dos Reis, Lukasz Szpruch

TL;DR
This paper develops a framework for analyzing modified explicit schemes for BSDEs with polynomial growth, enabling convergence and qualitative property preservation with reduced computational effort compared to implicit schemes.
Contribution
It introduces a systematic framework for analyzing modified explicit schemes for BSDEs with polynomial growth, achieving convergence and qualitative properties similar to implicit schemes.
Findings
Modified explicit schemes can converge with the same rate as implicit schemes.
The framework ensures preservation of qualitative properties like comparison theorems.
Modified explicit schemes can outperform implicit schemes computationally.
Abstract
The theory of Forward-Backward Stochastic Differential Equations (FBSDEs) paves a way to probabilistic numerical methods for nonlinear parabolic PDEs. The majority of the results on the numerical methods for FBSDEs relies on the global Lipschitz assumption, which is not satisfied for a number of important cases such as the Fisher--KPP or the FitzHugh--Nagumo equations. Furthermore, it has been shown in \cite{LionnetReisSzpruch2015} that for BSDEs with monotone drivers having polynomial growth in the primary variable , only the (sufficiently) implicit schemes converge. But these require an additional computational effort compared to explicit schemes. This article develops a general framework that allows the analysis, in a systematic fashion, of the integrability properties, convergence and qualitative properties (e.g.~comparison theorem) for whole families of modified explicit…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and financial applications · Risk and Portfolio Optimization · Probabilistic and Robust Engineering Design
