Variation and efficiency of high-frequency betas

Published

Journal Article

© 2020 Elsevier B.V. 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.

Full Text

Duke Authors

Cited Authors

  • Zhang, C; Li, J; Todorov, V; Tauchen, G

Published Date

  • January 1, 2020

Published In

Electronic International Standard Serial Number (EISSN)

  • 1872-6895

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/j.jeconom.2020.05.022

Citation Source

  • Scopus