Stationary, Markov, stochastic processes with polynomial conditional moments and continuous paths

TL;DR

This paper investigates stationary Markov processes with polynomial conditional moments that can be modified to have continuous paths, simplifying simulation and analysis of such stochastic processes.

Contribution

It provides a simple criterion based on skewness and kurtosis for determining when these processes have continuous path modifications.

Findings

Processes include Ornstein-Uhlenbeck, Gamma, and Arcsin margin processes.

Criterion relates skewness and kurtosis to path continuity.

Facilitates easier simulation of complex stochastic models.

Abstract

We are studying stationary random processes with conditional polynomial moments that allow a continuous path modification. Processes with continuous path modification, are important because they are relatively easy to simulate. One does not have to care about the distribution of their jumps which is always difficult to find. Among them are the Ornstein-Uhlenbeck process, the Gamma process, the process with Arcsin margins and the Theta function transition densities and others. We give a simple criterion for the stationary process to have a continuous path modification expressed in terms of skewness and excess kurtosis of the marginal distribution.