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Bias-corrected realized variance

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  • Jin-Huei Yeh
  • Jying-Nan Wang

Abstract

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.

Suggested Citation

  • Jin-Huei Yeh & Jying-Nan Wang, 2019. "Bias-corrected realized variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 170-192, February.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:2:p:170-192
    DOI: 10.1080/07474938.2016.1222230
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    Cited by:

    1. Sucarrat, Genaro, 2020. "Identification of Volatility Proxies as Expectations of Squared Financial Return," MPRA Paper 101953, University Library of Munich, Germany.
    2. Wang, Jying-Nan & Vigne, Samuel A. & Liu, Hung-Chun & Hsu, Yuan-Teng, 2024. "Hacks and the price synchronicity of bitcoin and ether," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 294-299.
    3. Reschenhofer, Erhard & Mangat, Manveer Kaur & Stark, Thomas, 2020. "Volatility forecasts, proxies and loss functions," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 133-153.
    4. Sucarrat, Genaro, 2021. "Identification of volatility proxies as expectations of squared financial returns," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1677-1690.

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