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) models. We compare the daily volatilities estimated using the ACD models against several versions of the realized volatility (RV) method, including the bipower variation realized volatility with subsampling, the realized kernel estimate and the duration-based realized volatility. The ACD volatility estimates correlate highly with and perform very well against the RV estimates. Our Monte Carlo results show that our method has lower root mean-squared error than the RV methods in most cases. A clear advantage of our method is that it can be used to estimate intraday volatilities over intervals such as an hour or 15 minutes.
Autoregressive Conditional Duration, Market Microstructure, Realized Volatility, Semiparametric Method, Transaction Data
Tse, Yiu Kuen and Yang, Tao.
Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Models Approach. (2010). 1-47. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1276
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