Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2024
Abstract
We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman's recent congressional testimony.
Keywords
Range-based estimation, microstructure noise, inference, Semimartingale, Volatility
Discipline
Econometrics
Research Areas
Econometrics
Publication
Review of Economics and Statistics
Volume
106
Issue
4
First Page
1114
Last Page
1128
ISSN
0034-6535
Identifier
10.1162/rest_a_01203
Publisher
Massachusetts Institute of Technology Press (MIT Press): 12 month embargo
Citation
LI, Jia; WANG, Dishen; and ZHANG, Qiushi.
Reading the candlesticks: An OK estimator for volatility. (2024). Review of Economics and Statistics. 106, (4), 1114-1128.
Available at: https://ink.library.smu.edu.sg/soe_research/2565
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/10.1162/rest_a_01203