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

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  • Kim Christensen

    ()

  • Silja Kinnebrock

    ()
    (University of Oxford - University of Oxford)

  • Mark Podolskij

    ()

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.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number peer-00732537.

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Date of creation: 15 Sep 2010
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Publication status: Published, Journal of Econometrics, 2010, 159, 1, 116
Handle: RePEc:hal:journl:peer-00732537

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Related research

Keywords: C10; C22; C80; Central limit theorem; Diffusion models; High-frequency data; Market microstructure noise; Non-synchronous trading; Pre-averaging; Realised covariance;

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