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A Test for Dependence and Covariance Estimator of Market Microstructure Noise

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  • Masato Ubukata

    ()
    (Graduate School of Economics, Osaka University)

  • Kosuke Oya

    ()
    (Graduate School of Economics, Osaka University)

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    Abstract

    There are many approaches for estimating an integrated variance and covariance in the presence of market microstructure noise. It is important to know a dependence of noise to construct the integrated variance and covariance estimators. We study a time dependence of bivariate noise processes in this paper. We propose a test statistic for the dependence of the noises and an autocovariance estimator of the noises and derive its asymptotic distribution. The asymptotic distribution of the autocovariance estimator provides us to another test statistic which is for significance of the autocovariances and for detection whether the noise exists or not. We obtain good performances of the test statistics and autocovariance estimator of the noises in a finite sample through Monte Carlo simulation. In empirical illustration, we confirm that the proposed statistics and estimators capture various dependence patterns of the market microstructure noises.

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    Bibliographic Info

    Paper provided by Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP) in its series Discussion Papers in Economics and Business with number 07-03-Rev.2.

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    Length: 40 pages
    Date of creation: Mar 2008
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    Handle: RePEc:osk:wpaper:0703r2

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    Web page: http://www.econ.osaka-u.ac.jp/
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    Related research

    Keywords: test statistic; market microstructure noise; time-dependence; nonsynchronous observations; high frequency data.;

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    References

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    1. Yacine Ait-Sahalia & Per A. Mykland & Lan Zhang, 2005. "Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise," NBER Working Papers 11380, National Bureau of Economic Research, Inc.
    2. Neil Shephard & Ole E. Barndorff-Nielsen, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Series Working Papers 2006-W03, University of Oxford, Department of Economics.
    3. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.
    4. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 79(3), pages 655-692, March.
    5. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 24, pages 127-161, April.
    6. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    7. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 525-554.
    8. Politis, D. N. & Romano, Joseph P. & Wolf, Michael, 1997. "Subsampling for heteroskedastic time series," Journal of Econometrics, Elsevier, Elsevier, vol. 81(2), pages 281-317, December.
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