Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process

TL;DR

This paper develops a nonparametric estimator for the drift of a diffusion process with jumps driven by a Hawkes process, using high-frequency data and a least squares approach, with theoretical and simulation validation.

Contribution

It introduces a novel nonparametric drift estimator for diffusions with Hawkes process-driven jumps, combining high-frequency observations and adaptive least squares methods.

Findings

Estimator performs well in simulations.

Adaptive results are comparable to nonparametric regression.

Effective in high-frequency data settings.

Abstract

We consider a 1-dimensional diffusion process X with jumps. The particularity of this model relies in the jumps which are driven by a multidimensional Hawkes process denoted N. This article is dedicated to the study of a nonparametric estimator of the drift coefficient of this original process. We construct estimators based on discrete observations of the process X in a high frequency framework with a large horizon time and on the observations of the process N. The proposed nonparametric estimator is built from a least squares contrast procedure on subspace spanned by trigonometric basis vectors. We obtain adaptive results that are comparable with the one obtained in the nonparametric regression context. We finally conduct a simulation study in which we first focus on the implementation of the process and then on showing the good behavior of the estimator.