Nonuniform-Grid Markov Chain Approximation of Continuous Processes with Time-Linear Moments

TL;DR

This paper introduces a nonuniform-grid Markov chain method that accurately approximates continuous stochastic processes with linear time-dependent moments, enabling efficient simulation and analysis.

Contribution

It presents a novel approach for moment-matching Markov chain approximation on nonuniform grids for processes with linear mean and variance functions.

Findings

Accurately approximates heat diffusion and GBM processes.

Maintains key process characteristics through moment matching.

Achieves small Wasserstein-1 distances in numerical simulations.

Abstract

We propose a method to approximate continuous-time, continuous-state stochastic processes by a discrete-time Markov chain defined on a nonuniform grid. Our method provides exact moment matching for processes whose first and second moments are linear functions of time. In particular, we show that, under certain conditions, the transition probabilities of a Markov chain can be chosen so that its first two moments match prescribed linear functions of time. These conditions depend on the grid points of the Markov chain and the coefficients of the linear mean and variance functions. Our proof relies on two recurrence relations for the expectation and variance across time. This approach enables simulation-based numerical analysis of continuous processes while preserving their key characteristics. We illustrate its efficacy by approximating continuous processes describing heat diffusion and geometric Brownian motion (GBM). For heat diffusion, we show that the heat profile at a set of points can be investigated by embedding those points inside the nonuniform grid of our Markov chain. For GBM, numerical simulations demonstrate that our approach, combined with suitable nonuniform grids, yields accurate approximations, with consistently small empirical Wasserstein-1 distances at long time horizons.