Bias-corrected realized variance

Jin Huei Yeh, Jying Nan Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We propose a novel “bias-corrected realized variance” (BCRV) estimator based upon the appropriate re-weighting of two realized variances calculated at different sampling frequencies. Our bias-correction methodology is found to be extremely accurate, with the finite sample variance being significantly minimized. In our Monte Carlo experiments and a finite sample MSE comparison of alternative estimators, the performance of our straightforward BCRV estimator is shown to be comparable to other widely-used integrated variance estimators. Given its simplicity, our BCRV estimator is likely to appeal to researchers and practitioners alike for the estimation of integrated variance.

Original languageEnglish
Pages (from-to)170-192
Number of pages23
JournalEconometric Reviews
Issue number2
StatePublished - 7 Feb 2019


  • Bias correction
  • finite sample MSE
  • market microstructure noise
  • optimal sampling frequency
  • realized variance


Dive into the research topics of 'Bias-corrected realized variance'. Together they form a unique fingerprint.

Cite this