Reading the candlesticks: An OK estimator for volatility

Author

Jia LI, Singapore Management UniversityFollow
Dishen WANG
Qiushi ZHANG, University of International Business and Economics

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