Testing for self-excitation in financial events: A bayesian approach

Abstract

Self-exciting temporal point processes are used to model a variety of financial event data including order flows, trades, and news. In this work, we take a Bayesian approach to inference and model comparison in self-exciting processes. We discuss strategies to compute marginal likelihood estimates for the univariate Hawkes process, and describe a Bayesian model comparison scheme. We demonstrate on currency, cryptocurrency and equity limit order book data that the test captures excitatory dynamics.