Variation and efficiency of high-frequency betas

Congshan Zhang, Jia Li, Viktor Todorov*, George Tauchen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalJournal of Econometrics
StateAccepted/In press - 2020


  • Adaptive estimation
  • Beta
  • High frequency data
  • Jump
  • Semiparametric efficiency
  • Volatility

ASJC Scopus subject areas

  • Economics and Econometrics

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