Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach
AbstractWe 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.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.
Volume (Year): 30 (2012)
Issue (Month): 4 (April)
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Web page: http://www.tandfonline.com/UBES20
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- DANIEL PREVE & Yiu-Kuen Tse, 2012. "Estimation Of Time Varying Adjusted Probability Of Informed Trading And Probability Of Symmetric Order-Flow Shock," Working Papers CoFie-05-2011, Sim Kee Boon Institute for Financial Economics.
- Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
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