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Finite Sample Analysis of Weighted Realized Covariance with Noisy Asynchronous Observations

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Author Info
Taro Kanatani () (Institute of Economic Research, Kyoto University)

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Abstract

In this paper, we provide a framework to evaluate finite sample MSE of several realized covariance estimators when using nonsynchronous observations contaminated with microstructure noise. This framework enables us to examine different estimators. We propose some estimators as an application of the framework.

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File URL: http://www.kier.kyoto-u.ac.jp/DP/DP634.pdf
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Publisher Info
Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 634.

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Length: 19pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:kyo:wpaper:634

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Related research
Keywords: High frequency data; Weighted realized covariance; Nonsynchronous (asynchronous) observation; Microstructure noise;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Neil Shephard & Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Series Working Papers 264, University of Oxford, Department of Economics. [Downloadable!]
    Other versions:
  2. Taro Kanatani & Roberto Reno', 2007. "Unbiased covariance estimation with interpolated data," Department of Economics University of Siena 502, Department of Economics, University of Siena. [Downloadable!]
  3. Masato Ubukata & Kosuke Oya, 2007. "Test of Unbiasedness of the Integrated Covariance Estimation in the Presence of Noise," Discussion Papers in Economics and Business 07-03, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
  4. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 68-104. [Downloadable!] (restricted)
  5. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
    Other versions:
  6. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61. [Downloadable!] (restricted)
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