Functional Laplace Transform of a Multivariate Hawkes Process, Subsequent Characteristics, and Numerical Approximations

TL;DR

This paper develops a comprehensive mathematical framework for multivariate Hawkes processes with time-dependent baseline intensities and general excitation functions, providing explicit formulas for characteristic functions, moments, and covariance structures, along with numerical methods and simulations.

Contribution

It introduces closed-form expressions for the characteristic function, moments, and covariance of a broad class of non-Markovian multivariate Hawkes processes, extending existing theoretical results.

Findings

Derived the multivariate multi-temporal characteristic function.

Established formulas for the first two moments and covariance structure.

Presented numerical schemes and simulations for the proposed models.

Abstract

Numerous studies grounded on Hawkes processes have been carried out in many fields including finance, biology and social network. Hawkes processes form a class of selfexciting simple point processes. In this article, we consider a general class of multivariate Hawkes processes envisioned to model dynamics of spatio-temporal epidemics. For this class, the igniting baseline intensity is time dependent and the exciting matrix function is a general one, making the model non-Markovian in most of the cases. In this article, we first provide the closed-form expression of the multivariate multi-temporal characteristic function of these Hawkes processes, extending in a natural way the classical single-time formula found in the Hawkes literature. Then, we use the infinitely divisible property of the Hawkes process to derive the equation system related to the probability distribution of counts at each single time, adapted to the general formulation of the Hawkes model considered in this article. Next, we provide closed-form formulas for the temporal structure of the two first moments of the process, which allows us to deduce an original expression of the multivariate covariance function at two distinct times, thereby extending existing results established for more restricted classes of Hawkes processes. Based on this expression, we analytically decompose the covariance at two distinct times into singular and continuous parts. We finish with brief numerical elements: We present a simple scheme for numerical approximations of the Laplace transform and the first two moments, and give examples of solutions of the different related integral equations. We also provides illustrative simulations of the multivariate Hawkes process for different model specifications.