Modelling systemic price cojumps with Hawkes factor models

TL;DR

This paper introduces a Hawkes one factor model to effectively capture the clustering and synchronization of high-frequency price cojumps across multiple financial assets, addressing limitations of existing models.

Contribution

The paper proposes a novel Hawkes one factor model that better describes systemic price cojumps than traditional multivariate Poisson or Hawkes models.

Findings

The model captures jump clustering effectively.

It explains high synchronization of cojumps across assets.

Outperforms existing models in fitting high-frequency jump data.

Abstract

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.