Variation and efficiency of high-frequency betas

Congshan Zhang, Jia Li, Viktor Todorov and George Tauchen

Journal of Econometrics, 2022, vol. 228, issue 1, 156-175

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 (search for similar items in EconPapers)
JEL-codes: C51 C52 G12 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:228:y:2022:i:1:p:156-175

DOI: 10.1016/j.jeconom.2020.05.022

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