Wong-Zakai approximations for quasilinear systems of Ito's type stochastic differential equations driven by fBm with H > 1 2

Pub Date : 2021-11-09 DOI:10.1142/s0219025723500224
Ramiro Scorolli
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Abstract

In a recent article Lanconelli and Scorolli (2021) extended to the multidimensional case a Wong-Zakai-type approximation for It\^o stochastic differential equations proposed by \Oksendal and Hu (1996). The aim of the current paper is to extend the latter result to system of stochastic differential equations of It\^o type driven by fractional Brownian motion (fBm) like those considered by Hu (2018). The covariance structure of the fBm precludes us from using the same approach as that used by Lanconelli and Scorolli and instead we employ a truncated Cameron-Martin expansion as the approximation for the fBm. We are naturally led to the investigation of a semilinear hyperbolic system of evolution equations in several space variables that we utilize for constructing a solution of the Wong-Zakai approximated systems. We show that the law of each element of the approximating sequence solves in the sense of distribution a Fokker-Planck equation and that the sequence converges to the solution of the Ito\^o equation, as the number of terms in the expansion goes to infinite.
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由H > 12的fBm驱动的Ito型随机微分方程拟线性系统的Wong-Zakai逼近
在最近的一篇文章中,Lanconelli和Scorolli(2021)将Oksendal和Hu(1996)提出的It\^o随机微分方程的wong - zakai型近似扩展到多维情况。本文的目的是将后一种结果扩展到由分数布朗运动(fBm)驱动的It\^o型随机微分方程系统,就像Hu(2018)所考虑的那样。fBm的协方差结构使我们无法使用与Lanconelli和Scorolli使用的方法相同的方法,而是使用截断的Cameron-Martin展开作为fBm的近似。我们很自然地研究了一个由若干空间变量组成的半线性双曲演化方程系统,我们利用它来构造Wong-Zakai近似系统的解。我们证明了逼近序列的每个元素的定律在分布意义上解决了一个福克-普朗克方程,并且当展开式中的项数趋于无穷时,该序列收敛于Ito ^o方程的解。
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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