具有奇异漂移的路径分布依赖随机微分方程的良好拟合

Pub Date : 2024-07-14 DOI:10.1007/s10959-024-01356-y
Xiao-Yu Zhao
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引用次数: 0

摘要

本文推导了具有非退化噪声的奇异路径依赖分布随机微分方程(SDE)的良好求解性,其中允许漂移在当前状态下是奇异的,但在历史路径上保持局部利普希兹连续性,并且系数相对于分布变量的加权变化距离是利普希兹连续的。值得注意的是,即使对于系数与分布无关的经典路径依赖 SDE,这一结果也是全新的。此外,通过将局部 Lipschitz 连续性加强为 Lipschitz 连续性,并用 Wasserstein 距离代替加权变化距离,我们还获得了良好拟合。
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Well-Posedness for Path-Distribution Dependent Stochastic Differential Equations with Singular Drifts

Well-posedness is derived for singular path-distribution dependent stochastic differential equations (SDEs) with non-degenerate noise, where the drift is allowed to be singular in the current state, but maintains local Lipschitz continuity in the historical path, and the coefficients are Lipschitz continuous with respect to a weighted variation distance in the distribution variable. Notably, this result is new even for classical path-dependent SDEs where the coefficients are distribution independent. Moreover, by strengthening the local Lipschitz continuity to Lipschitz continuity and replacing the weighted variation distance with the Wasserstein distance, we also obtain well-posedness.

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