L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients

Abstract

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.

References in corpus (3)

• Non-standard approximations of the Ito-map
• McKean-Vlasov SDEs in nonlinear filtering
• A Wong-Zakai Approximation of Stochastic Differential Equations Driven by a General Semimartingale

Cited by in corpus (2)

• McKean-Vlasov SDEs in nonlinear filtering
• Analysis of the feedback particle filter with diffusion map based approximation of the gain