Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
Statistics Theory
2021-08-23 v3 Machine Learning
Statistics Theory
Abstract
In this paper, we present a maximum likelihood method for estimating the parameters of a univariate Hawkes process with self-excitation or inhibition. Our work generalizes techniques and results that were restricted to the self-exciting scenario. The proposed estimator is implemented for the classical exponential kernel and we show that, in the inhibition context, our procedure provides more accurate estimations than current alternative approaches.
Keywords
Cite
@article{arxiv.2103.05299,
title = {Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition},
author = {Anna Bonnet and Miguel Martinez Herrera and Maxime Sangnier},
journal= {arXiv preprint arXiv:2103.05299},
year = {2021}
}