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Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach


  • Yiu-kuen Tse
  • Thomas Tao Yang


We propose a method to estimate the intraday volatility of a stock by integrating the instantaneous conditional return variance per unit time obtained from the autoregressive conditional duration (ACD) model, called the ACD-ICV method. We compare the daily volatility estimated using the ACD-ICV method against several versions of the realized volatility (RV) method, including the bipower variation RV with subsampling, the realized kernel estimate, and the duration-based RV. Our Monte Carlo results show that the ACD-ICV method has lower root mean-squared error than the RV methods in almost all cases considered. This article has online supplementary material.

Suggested Citation

  • Yiu-kuen Tse & Thomas Tao Yang, 2012. "Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 533-545, April.
  • Handle: RePEc:taf:jnlbes:v:30:y:2012:i:4:p:533-545
    DOI: 10.1080/07350015.2012.707582

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    Cited by:

    1. Tse, Yiu-Kuen & Dong, Yingjie, 2014. "Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 352-361.
    2. Liu, Shouwei & Tse, Yiu-Kuen, 2015. "Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach," Journal of Econometrics, Elsevier, vol. 189(2), pages 437-446.
    3. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    4. Dong, Yingjie & Tse, Yiu-Kuen, 2017. "On estimating market microstructure noise variance," Economics Letters, Elsevier, vol. 150(C), pages 59-62.
    5. repec:gam:jecnmx:v:5:y:2017:i:4:p:51-:d:118613 is not listed on IDEAS

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