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Model-based clustering of diffusion processes with constant volatility coefficients using high-frequency data
Abstract
This paper considers model-based clustering of diffusion processes using high-frequency data. We formulate a finite mixture model for diffusion processes and develop a clustering method based on their constant diffusion coefficients. Using a quasi-likelihood, we propose estimators of the number of clusters, the diffusion coefficients and the cluster labels, and establish their consistency and the asymptotic normality of the diffusion coefficient estimators under high-frequency asymptotics with the number of observed processes fixed. Simulation studies are conducted to investigate the finite-sample performance of the proposed method.
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BibTeX
@misc{kusano2026modelbased,
title = {{Model-based clustering of diffusion processes with constant volatility coefficients using high-frequency data}},
author = {Shogo Kusano and Masayuki Uchida},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37514},
eprint = {2610.37514},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37514},
}
RIS
TY - UNPB
TI - Model-based clustering of diffusion processes with constant volatility coefficients using high-frequency data
AU - Shogo Kusano
AU - Masayuki Uchida
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37514
UR - https://arxiv.org/abs/2610.37514
AB - This paper considers model-based clustering of diffusion processes using high-frequency data. We formulate a finite mixture model for diffusion processes and develop a clustering method based on their constant diffusion coefficients. Using a quasi-likelihood, we propose estimators of the number of clusters, the diffusion coefficients and the cluster labels, and establish their consistency and the asymptotic normality of the diffusion coefficient estimators under high-frequency asymptotics with the number of observed processes fixed. Simulation studies are conducted to investigate the finite-sample performance of the proposed method.
ER -