Improved Berry-Esseen bounds for parameter estimation of a fractional Ornstein-Uhlenbeck process observed at high frequency

This paper deals with parameter estimation of the drift coefficient, denoted θ, for a fractional Ornstein-Uhlenbeck process observed at high frequency. Specifically, we provide an estimator of θ, namely $\widetilde{\theta }_{n}$, using the second moment method. We develop some novel estimates to derive an explicit Berry–Esseen bound in the Wasserstein distance for the normal approximation of $\widetilde{\theta }_{n}$. Moreover, we prove that our estimate is strictly sharper than the one obtained in Douissi et al. (Electron. J. Stat. 16(1):636–670, 2022).

Authors

Institutions

Publication Details

Journal
Advances in Continuous and Discrete Models
Published
2026-01-29
DOI
https://doi.org/10.1186/s13662-026-04060-9
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improved Berry-Esseen bounds for parameter estimation of a fractional Ornstein-Uhlenbeck process observed at high frequency

Mishari Al-Foraih, Fares Alazemi, Khalifa Es-Sebaiy
Advances in Continuous and Discrete Models
Stochastic processes and financial applications
article

Improved Berry-Esseen bounds for parameter estimation of a fractional Ornstein-Uhlenbeck process observed at high frequency

Mishari Al-Foraih, Fares Alazemi, Khalifa Es-Sebaiy
article en

Abstract

This paper deals with parameter estimation of the drift coefficient, denoted θ, for a fractional Ornstein-Uhlenbeck process observed at high frequency. Specifically, we provide an estimator of θ, namely $\widetilde{\theta }_{n}$, using the second moment method. We develop some novel estimates to derive an explicit Berry–Esseen bound in the Wasserstein distance for the normal approximation of $\widetilde{\theta }_{n}$. Moreover, we prove that our estimate is strictly sharper than the one obtained in Douissi et al. (Electron. J. Stat. 16(1):636–670, 2022).

Advances in Continuous and Discrete ModelsVol. 2026(1)
Kuwait University (KW)
Openalex Percentile: Top 76%
Stochastic processes and financial applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.