Abstract
Univariate marked Hawkes processes are used to model a range of real‐world phenomena including earthquake aftershock sequences, contagious disease spread, content diffusion on social media platforms, and order book dynamics. This paper illustrates a fundamental connection between univariate marked Hawkes processes and multivariate Hawkes processes. Exploiting this connection renders a framework that can be built upon for expressive and flexible inference on diverse data. Specifically, multivariate unmarked Hawkes representations are introduced as a tool to parameterize univariate marked Hawkes processes. We show that such multivariate representations can asymptotically approximate a large class of univariate marked Hawkes processes, are stationary given the approximated process is stationary, and that resultant conditional intensity parameters are identifiable and, more importantly, interpretable. A simulation study provides a heuristic bound for error induced by the relatively larger parameter space of multivariate Hawkes processes, and an application to the Southern California earthquake catalogue is presented to demonstrate the efficacy of our novel approach.