PreprintCC BY 4.0HAL
Gaussian approximation for the integrated intensity of counting processes: application to Hawkes processes
Abstract
In this paper, we establish quantitative central limit theorems for the integrated fluctuations of the intensity of a counting process. Building on the work of Coutin and using the Clark-Ocone formula, we first derive a general upper bound on the Wasserstein distance between a Gaussian random variable and the normalized integral of the centered intensity. We then apply this result to subcritical linear Hawkes processes. In this setting, we prove that the integrated fluctuations of the intensity converge in distribution to a centered Gaussian random variable, with a convergence rate governed by the tail behavior of the resolvent associated with the underlying Volterra equation.
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BibTeX
@misc{massat2026gaussian,
title = {{Gaussian approximation for the integrated intensity of counting processes: application to Hawkes processes}},
author = {Benjamin Massat},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.19737},
eprint = {2610.19737},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05794951v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Gaussian approximation for the integrated intensity of counting processes: application to Hawkes processes
AU - Benjamin Massat
PY - 2026
DA - 2026/10/12
PB - HAL
DO - 10.48550/arxiv.2610.19737
UR - https://hal.science/hal-05794951v1
AB - In this paper, we establish quantitative central limit theorems for the integrated fluctuations of the intensity of a counting process. Building on the work of Coutin and using the Clark-Ocone formula, we first derive a general upper bound on the Wasserstein distance between a Gaussian random variable and the normalized integral of the centered intensity. We then apply this result to subcritical linear Hawkes processes. In this setting, we prove that the integrated fluctuations of the intensity converge in distribution to a centered Gaussian random variable, with a convergence rate governed by the tail behavior of the resolvent associated with the underlying Volterra equation.
ER -