Covariance Measurement in the Presence of Non-Synchronous Trading and Market Microstructure Noise

25 Pages Posted: 7 Jul 2006 Last revised: 15 Dec 2010

See all articles by Jim E. Griffin

Jim E. Griffin

University of Kent; University of Kent - School of Mathematics, Statistics and Actuarial Science

Roel C. A. Oomen

Deutsche Bank AG (London); London School of Economics & Political Science (LSE) - Department of Statistics

Date Written: July 1, 2009

Abstract

This paper studies the problem of covariance estimation when prices are observed non-synchronously and contaminated by i.i.d. microstructure noise. We derive closed form expressions for the bias and variance of three popular covariance estimators, namely realised covariance, realised covariance plus lead- and lag-adjustments, and the Hayashi and Yoshida estimator, and present a comprehensive investigation into their properties and relative efficiency. Our main finding is that the ordering of the covariance estimators in terms of efficiency crucially depends on the level of microstructure noise, as well as the level of correlation. In fact, for sufficiently high levels of noise, the standard realised covariance estimator (without any corrections for non-synchronous trading) can be most efficient. We also propose a sparse sampling implementation of the Hayashi and Yoshida estimator, study the robustness of our findings using simulations with stochastic volatility and correlation, and highlight some important practical considerations.

Keywords: realized covariance, optimal sampling, lead-lag correlations, bias correction

JEL Classification: C14, C22, G10

Suggested Citation

Griffin, Jim E. and Oomen, Roel C.A., Covariance Measurement in the Presence of Non-Synchronous Trading and Market Microstructure Noise (July 1, 2009). Journal of Econometrics, Vol. 160, No. 1, pp. 58-68, 2011. Available at SSRN: https://ssrn.com/abstract=912541

Jim E. Griffin

University of Kent ( email )

Cornwallis Building
Canterbury, Kent CT2 7NF
United Kingdom

HOME PAGE: http://www.kent.ac.uk/ims/personal/jeg28/index.htm

University of Kent - School of Mathematics, Statistics and Actuarial Science ( email )

Cornwallis Building
Canterbury, CT2 7NF
United Kingdom

Roel C.A. Oomen (Contact Author)

Deutsche Bank AG (London) ( email )

Winchester House
1 Great Winchester Street
London, EC2N 2DB
United Kingdom

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

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