Large deviation principles and Malliavin derivative for mean reflected stochastic differential equations

In this paper, we consider a class of reflected stochastic differential equations for which the constraint is not on the paths of the solution but on its law. We establish a small noise large deviation principle, a large deviation for short time, the Malliavin derivative and the smoothness of the de...

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Published in:Stochastics (Abingdon, Eng. : 2005) Eng. : 2005), 2024-11, Vol.96 (7), p.1913-1927
Main Authors: Chen, Ping, Zhai, Jianliang
Format: Article
Language:English
Subjects:
Deviation
Differential equations
large derivative principle
Malliavin derivative
Mean reflected stochastic differential equation
Smoothness
weak convergence method
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Zhai, Jianliang
description In this paper, we consider a class of reflected stochastic differential equations for which the constraint is not on the paths of the solution but on its law. We establish a small noise large deviation principle, a large deviation for short time, the Malliavin derivative and the smoothness of the density. To prove large deviation principles, a sufficient condition for the weak convergence method, which is suitable for Mckean-Vlasov stochastic differential equation, plays an important role.
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source Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list)
subjects Deviation
Differential equations
large derivative principle
Malliavin derivative
Mean reflected stochastic differential equation
Smoothness
weak convergence method
title Large deviation principles and Malliavin derivative for mean reflected stochastic differential equations
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