Markov Jump Processes Approximating a Nonsymmetric Generalized Diffusion: numerics explained to probabilists

Abstract

Consider a non-symmetric generalized diffusion $X(\cdot )$ in ${\mathbb{R}}^d$ determined by the differential operator $A(\msx)=-\sum_{ij} \partial_ia_{ij}(\msx)\partial_j +\sum_i b_i(\msx)\partial_i$. In this paper the diffusion process is approximated by Markov jump processes $X_n(\cdot )$, in homogeneous and isotropic grids $G_n\subset {\mathbb{R}}^d$, which converge in distribution to the diffusion $X(\cdot )$. The generators of $X_n(\cdot )$ are constructed explicitly. Due to the homogeneity and isotropy of grids, the proposed method for $d\ge 3$ can be applied to processes for which the diffusion tensor $\{a_{ij}(\msx)\}_{11}^{dd}$ fulfills an additional condition. The proposed construction offers a simple method for simulation of sample paths of non-symmetric generalized diffusion. Simulations are carried out in terms of jump processes $X_n(\cdot )$. For $d=2$ the construction can be easily implemented into a computer code.

21 pages, 1 figure this is an extended version including detailed arguments and additional explanations of the analysis background, intended for a typical probabilist

References in corpus (1)

• A scheme for simulating one-dimensional diffusion processes with discontinuous coefficients