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Noise reduced realized volatility: a kalman filter approach

In: Econometric Analysis of Financial and Economic Time Series

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  • John P. Owens
  • Douglas G. Steigerwald

Abstract

Microstructure noise contaminates high-frequency estimates of asset price volatility. Recent work has determined a preferred sampling frequency under the assumption that the properties of noise are constant. Given the sampling frequency, the high-frequency observations are given equal weight. While convenient, constant weights are not necessarily efficient. We use the Kalman filter to derive more efficient weights, for any given sampling frequency. We demonstrate the efficacy of the procedure through an extensive simulation exercise, showing that our filter compares favorably to more traditional methods.

Suggested Citation

  • John P. Owens & Douglas G. Steigerwald, 2006. "Noise reduced realized volatility: a kalman filter approach," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 211-227, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(05)20008-7
    DOI: 10.1016/S0731-9053(05)20008-7
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    2. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    3. 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.
    4. Daisuke Nagakura & Toshiaki Watanabe, 2015. "A State Space Approach to Estimating the Integrated Variance under the Existence of Market Microstructure Noise," Journal of Financial Econometrics, Oxford University Press, vol. 13(1), pages 45-82.
    5. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    6. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    7. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.

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