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Approximation of stochastic differential equations driven by subfractional Brownian motion at discrete time observation
Communications in Statistics - Theory and Methods ( IF 0.8 ) Pub Date : 2021-03-23 , DOI: 10.1080/03610926.2021.1901924
Guangjun Shen 1, 2 , Zheng Tang 1 , Jun Wang 3
Affiliation  

Abstract

In this paper, we consider discrete time approximations for stochastic differential equations with the form: Xt=X0+0tf(Xs)dhs+0tg(Xs)dYsH,t>0, where h:R+R is a continuous function with locally bounded variation, f,g:RR are measurable functions, and the integral with respect to YtH=0tσsdSsH is the pathwise Riemann-Stieltjes integral, SH is a subfractional Brownian motion with H(12,1), σ is a deterministic (possibly discontinuous) function.



中文翻译:

离散时间观测下次分数布朗运动驱动的随机微分方程的逼近

摘要

在本文中,我们考虑具有以下形式的随机微分方程的离散时间近似:X=X0+0F(X)dH+0G(X)dH,>0,在哪里H:R+R是具有局部有界变化的连续函数,F,G:RR是可测函数,积分是关于H=0σd小号H是路径 Riemann-Stieltjes 积分,S H是次分数布朗运动,其中H(1个2个,1个), σ是确定性(可能不连续)函数。

更新日期:2021-03-23
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