A duality approach for the weak approximation of stochastic differential equations
arXiv:math/0610178 · doi:10.1214/105051606000000060
Abstract
In this article we develop a new methodology to prove weak approximation results for general stochastic differential equations. Instead of using a partial differential equation approach as is usually done for diffusions, the approach considered here uses the properties of the linear equation satisfied by the error process. This methodology seems to apply to a large class of processes and we present as an example the weak approximation of stochastic delay equations.
Published at http://dx.doi.org/10.1214/105051606000000060 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Cited by in corpus (8)
- Duality in refined Sobolev-Malliavin spaces and weak approximations of SPDE
- Weak convergence rates for an explicit full-discretization of stochastic Allen-Cahn equation with additive noise
- Weak convergence of path-dependent SDEs with irregular coefficients
- Existence and uniqueness of solutions of stochastic functional differential equations
- Kolmogorov Equations and Weak Order Analysis for SPDES with Nonlinear Diffusion Coefficient
- A new proof for the convergence of Picard's filter using partial Malliavin calculus
- Poisson Malliavin calculus in Hilbert space with an application to SPDE
- Localization of Wiener Functionals of Fractional Regularity and Applications