Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2012
Abstract
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.
Keywords
Market microstructure, Realized volatility, Semiparametric method, Transaction data
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Business and Economics Statistics
Volume
30
Issue
4
First Page
533
Last Page
545
ISSN
0735-0015
Identifier
10.1080/07350015.2012.707582
Publisher
Taylor and Francis
Citation
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.
Available at: https://ink.library.smu.edu.sg/soe_research/1410
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2012.707582