Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data

TL;DR

This paper introduces pre-averaging estimators for measuring the ex-post covariance matrix of financial assets in noisy, high-frequency data with non-synchronous trading, providing asymptotic theory and practical implementation methods.

Contribution

It develops a novel pre-averaged realized covariance estimator with optimal convergence and positive semi-definiteness, along with a noise-robust Hayashi-Yoshida estimator for non-synchronous data.

Findings

Estimator achieves optimal convergence rates.

Proposed methods are robust to microstructure noise.

Effective in high-frequency equity data analysis.

Abstract

We show how pre-averaging can be applied to the problem of measuring the ex-post covariance of financial asset returns under microstructure noise and non-synchronous trading. A pre-averaged realised covariance is proposed, and we present an asymptotic theory for this new estimator, which can be configured to possess an optimal convergence rate or to ensure positive semi-definite covariance matrix estimates. We also derive a noise-robust Hayashi-Yoshida estimator that can be implemented on the original data without prior alignment of prices. We uncover the finite sample properties of our estimators with simulations and illustrate their practical use on high-frequency equity data.