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A Test for Dependence and Covariance Estimator of Market Microstructure Noise

  • Masato Ubukata

    ()

    (Graduate School of Economics, Osaka University)

  • Kosuke Oya

    ()

    (Graduate School of Economics, Osaka University)

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    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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    File URL: http://www2.econ.osaka-u.ac.jp/library/global/dp/0703R2.pdf
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    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
    Date of revision:
    Handle: RePEc:osk:wpaper:0703r2
    Contact details of provider: Web page: http://www.econ.osaka-u.ac.jp/
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    1. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    2. 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.
    3. 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.
    4. Aït-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2011. "Ultra high frequency volatility estimation with dependent microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 160-175, January.
    5. Politis, D. N. & Romano, Joseph P. & Wolf, Michael, 1997. "Subsampling for heteroskedastic time series," Journal of Econometrics, Elsevier, vol. 81(2), pages 281-317, December.
    6. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    7. Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2003. "A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data," NBER Working Papers 10111, National Bureau of Economic Research, Inc.
    8. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
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