The non-locality of Markov chain approximations to two-dimensional diffusions

TL;DR

This paper explores how Markov chain approximations to 2D diffusions are limited by non-locality, especially with high correlations or anisotropy, affecting their accuracy and convergence.

Contribution

It introduces a new formula quantifying the non-locality in Markov chain approximations for correlated anisotropic diffusions.

Findings

Transition probabilities must allow for distant moves in highly correlated cases.

Approximation difficulty increases with stronger correlations and anisotropy.

Implications for convergence analysis of related stochastic control problems.

Abstract

In this short paper, we consider discrete-time Markov chains on lattices as approximations to continuous-time diffusion processes. The approximations can be interpreted as finite difference schemes for the generator of the process. We derive conditions on the diffusion coefficients which permit transition probabilities to match locally first and second moments. We derive a novel formula which expresses how the matching becomes more difficult for larger (absolute) correlations and strongly anisotropic processes, such that instantaneous moves to more distant neighbours on the lattice have to be allowed. Roughly speaking, for non-zero correlations, the distance covered in one timestep is proportional to the ratio of volatilities in the two directions. We discuss the implications to Markov decision processes and the convergence analysis of approximations to Hamilton-Jacobi-Bellman equations in the Barles-Souganidis framework.