On near-martingales and a class of anticipating linear stochastic differential equations

Pub Date : 2023-12-16 DOI:10.1142/s0219025723500297
Hui-Hsiung Kuo, Pujan Shrestha, Sudip Sinha, Padmanabhan Sundar
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Abstract

The goals of this paper are to prove a near-martingale optional stopping theorem and establish solvability and large deviations for a class of anticipating linear stochastic differential equations. For a class of anticipating linear stochastic differential equations, we prove the existence and uniqueness of solutions using two approaches: (1) Ayed–Kuo differential formula using an ansatz, and (2) a braiding technique by interpreting the integral in the Skorokhod sense. We establish a Freidlin–Wentzell type large deviations result for the solution of such equations. In addition, we prove large deviation results for small noise where the initial conditions are random.

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关于近马丁格尔和一类预期线性随机微分方程
本文的目标是证明近马勒可选停顿定理,并建立一类预期线性随机微分方程的可解性和大偏差。对于一类预期线性随机微分方程,我们用两种方法证明了解的存在性和唯一性:(1) Ayed-Kuo 微分公式,使用 ansatz;(2) 编织技术,在 Skorokhod 意义上解释积分。我们为此类方程的解建立了一个弗雷德林-温采尔型大偏差结果。此外,我们还证明了初始条件为随机的小噪声的大偏差结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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