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.
Market microstructure, Realized volatility, Semiparametric method, Transaction data
Journal of Business and Economics Statistics
Taylor and Francis
TSE, Yiu Kuen and YANG, Thomas Tao.
Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach. (2012). Journal of Business and Economics Statistics. 30, (4), 533-545. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1410
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.