Stationarity of Bivariate Dynamic Contagion Processes

TL;DR

This paper investigates the conditions for stationarity in Bivariate Dynamic Contagion Processes, which model complex systems influenced by external and internal factors, providing insights into their long-term behavior.

Contribution

It establishes the existence and uniqueness of stationary distributions for BDCP and characterizes their properties using Markov theory and branching system approximation.

Findings

Existence of a unique stationary distribution under specific conditions

Stationary increments of the BDCP are proven

Moments of the stationary intensity are derived

Abstract

The Bivariate Dynamic Contagion Processes (BDCP) are a broad class of bivariate point processes characterized by the intensities as a general class of piecewise deterministic Markov processes. The BDCP describes a rich dynamic structure where the system is under the influence of both external and internal factors modelled by a shot-noise Cox process and a generalized Hawkes process respectively. In this paper we mainly address the stationarity issue for the BDCP, which is important in applications. We investigate the stationary distribution by applying the the Markov theory on the branching system approximation representation of the BDCP. We find the condition under which there exists a unique stationary distribution of the BDCP intensity and the resulting BDCP has stationary increments. Moments of the stationary intensity are provided by using the Markov property.