Large deviations for generalized backward stochastic differential equations

2024/07/22 by Yawen Liu, Huijie Qiao, Liu, Yawen +1
Economics, Econometrics and Finance · #60H10 #FOS: Mathematics #Probability (math.PR) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2407.15584

openalex publication_date 2024/07/22 · openalex created_date 2025/01/05 · openalex updated_date 2026/07/28

Abstract

This work concerns generalized backward stochastic differential equations, which are coupled with a family of reflecting diffusion processes. First of all, we establish the large deviation principle for forward stochastic differential equations with reflecting boundaries under weak monotonicity conditions. Then based on the obtained result and the contraction principle, the large deviation principle for the generalized backward stochastic differential equations is proved. As a by-product, we obtain a limit result about parabolic partial differential equations with the nonlinear Neumann boundary conditions.

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