The aim of this paper is to obtain convergence in mean in the uniform topology of piecewise linear approximations of Stochastic Differential Equations (SDEs) with $C^1$ drift and $C^2$ diffusion coefficients with uniformly bounded derivatives. Convergence analyses for such Wong-Zakai approximations most often assume that the coefficients of the SDE are uniformly bounded. Almost sure convergence in the unbounded case can be obtained using now standard rough path techniques, although $L^q$ convergence appears yet to be established and is of importance for several applications involving Monte-Carlo approximations. We consider $L^2$ convergence in the unbounded case using a combination of traditional stochastic analysis and rough path techniques. We expect our proof technique extend to more general piecewise smooth approximations.
翻译:本文的目的是在单向线性直线近似值统一表层中取得平均的趋同,即小盘差异线性近似值(SDEs)与1美元漂移系数和2美元扩散系数(SDEs)的正值趋同;对此类黄扎凯近似值的趋同分析往往假定SDE的系数是一致的;几乎可以肯定,采用现在的标准粗路技术,可以在无约束案例中取得趋同,尽管似乎需要确定1美元趋同值,这对涉及蒙特卡罗近似值的若干应用具有重要意义;我们考虑利用传统的随机分析与粗路技术相结合,在无约束案例中考虑2美元趋同值;我们期望我们的证据技术将扩大到更普遍的小盘光滑近近似。