Pre-Averaging Estimators of the Ex-Post Covariance Matrix in Noisy Diffusion Models with Non-Synchronous Data
50 Pages Posted: 7 Oct 2009 Last revised: 26 May 2010
Date Written: April 2010
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.
Keywords: Central limit theorem, Diffusion models, High-frequency data, Market microstructure noise, Non-synchronous trading, Pre-averaging, Realised covariance
JEL Classification: C10, C22, C80
Suggested Citation: Suggested Citation