Strongly convergent homogeneous approximations to inhomogeneous Markov jump processes and applications

TL;DR

This paper introduces a novel approximation method for time-inhomogeneous Markov jump processes using homogeneous processes on a fine Poisson grid, with proven strong convergence and practical applications.

Contribution

It develops a new approximation technique that ensures strong convergence of inhomogeneous processes to homogeneous ones, with explicit convergence rates and applications.

Findings

Strong convergence in the Skorokhod $J_1$ metric is established.

Convergence rates are provided for the approximation.

Applications include hazard-rate estimation and ruin probabilities.

Abstract

The study of time-inhomogeneous Markov jump processes is a traditional topic within probability theory that has recently attracted substantial attention in various applications. However, their flexibility also incurs a substantial mathematical burden which is usually circumvented by using well-known generic distributional approximations or simulations. This article provides a novel approximation method that tailors the dynamics of a time-homogeneous Markov jump process to meet those of its time-inhomogeneous counterpart on an increasingly fine Poisson grid. Strong convergence of the processes in terms of the Skorokhod $J_1$ metric is established, and convergence rates are provided. Under traditional regularity assumptions, distributional convergence is established for unconditional proxies, to the same limit. Special attention is devoted to the case where the target process has one absorbing state and the remaining ones transient, for which the absorption times also converge. Some applications are outlined, such as univariate hazard-rate density estimation, ruin probabilities, and multivariate phase-type density evaluation.