Mean Field Backward Stochastic Differential Equations with Double Mean Reflections

Fuente: arXiv
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Main Authors: Li, Hanwu, Shi, Jin
Format: Preprint
Published: 2025
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author Li, Hanwu
Shi, Jin
author_facet Li, Hanwu
Shi, Jin
contents In this paper, we analyze the mean field backward stochastic differential equations (MFBSDEs) with double mean reflections, whose generator and constraints both depend on the distribution of the solution. When the generator is Lipschitz continuous, based on the backward Skorokhod problem with nonlinear constraints, we investigate the solvability of the doubly mean reflected MFBSDEs by constructing a contraction mapping. Furthermore, if the constraints are linear, the solution can also be constructed by a penalization method. For the case of quadratic growth, we obtain the existence and uniqueness results by using a fixed-point argument, the BMO martingale theory and the θ-method.
format Preprint
id arxiv_https___arxiv_org_abs_2501_10939
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Mean Field Backward Stochastic Differential Equations with Double Mean Reflections
Li, Hanwu
Shi, Jin
Probability
In this paper, we analyze the mean field backward stochastic differential equations (MFBSDEs) with double mean reflections, whose generator and constraints both depend on the distribution of the solution. When the generator is Lipschitz continuous, based on the backward Skorokhod problem with nonlinear constraints, we investigate the solvability of the doubly mean reflected MFBSDEs by constructing a contraction mapping. Furthermore, if the constraints are linear, the solution can also be constructed by a penalization method. For the case of quadratic growth, we obtain the existence and uniqueness results by using a fixed-point argument, the BMO martingale theory and the θ-method.
title Mean Field Backward Stochastic Differential Equations with Double Mean Reflections
topic Probability
url https://arxiv.org/abs/2501.10939