New Berry-Esseen bounds for parameter estimation of Gaussian processes observed at high frequency

TL;DR

This paper develops new Berry-Esseen bounds for the convergence rates of the second moment estimator in high-frequency observations of Gaussian processes, improving existing estimates and applying to Ornstein-Uhlenbeck processes.

Contribution

It introduces sharper Berry-Esseen bounds for parameter estimation of Gaussian processes observed at high frequency, with novel techniques and applications to Ornstein-Uhlenbeck models.

Findings

Sharper convergence rate bounds in Kolmogorov and Wasserstein distances.

Application to drift parameter estimation in Ornstein-Uhlenbeck processes.

Improved estimates over existing literature.

Abstract

The purpose of this paper is to estimate the limiting variance of asymptotically stationary Gaussian processes observed at high frequency, using the second moment estimator (SME). We study rates of convergence of the central limit theorem for the SME in terms of the total variation, Kolmogorov and Wasserstein distances, using some novel techniques and sharp estimates for cumulants. We apply our approach to provide Berry-Esseen bounds in Kolmogorov and Wasserstein distances for estimators of the drift parameters of Gaussian Ornstein-Uhlenbeck processes. Moreover, we prove that most of our estimates are strictly sharper than the ones obtained in the existing literature.