Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2022
Abstract
This paper studies the efficient estimation of betas from high-frequency return data on a fixed time interval. Under an assumption of equal diffusive and jump betas, we derive the semiparametric efficiency bound for estimating the common beta and develop an adaptive estimator that attains the efficiency bound. We further propose a Hausman type test for deciding whether the common beta assumption is true from the high-frequency data. In our empirical analysis we provide examples of stocks and time periods for which a common market beta assumption appears true and ones for which this is not the case. We further quantify empirically the gains from the efficient common beta estimation developed in the paper.
Keywords
Adaptive estimation, Beta, High frequency data, Jump, Semiparametric efficiency, Volatility
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
228
Issue
1
First Page
156
Last Page
175
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2020.05.022
Publisher
Elsevier
Citation
ZHANG, Congshan; LI, Jia; TODOROV, Viktor; and TAUCHEN, George.
Variation and efficiency of high-frequency betas. (2022). Journal of Econometrics. 228, (1), 156-175.
Available at: https://ink.library.smu.edu.sg/soe_research/2562
Copyright Owner and License
Publisher
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/10.1016/j.jeconom.2020.05.022