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Bias-correcting the realized range-based variance in the presence of market microstructure noise

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  • Kim Christensen

    ()

  • Mark Podolskij

    ()

  • Mathias Vetter

    ()

Abstract

Market microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. In this paper, we analyze the impact of microstructure noise on the realized range-based variance and propose a bias-correction to the rangestatistic. The new estimator is shown to be consistent for the integrated variance and asymptotically mixed Gaussian under simple forms of microstructure noise, and we can select an optimal partition of the high-frequency data in order to minimize its asymptotic conditional variance. The finite sample properties of our estimator are studied with Monte Carlo simulations and we implement it on high-frequency data from TAQ. We find that a bias-corrected range-statistic often has much smaller confidence intervals than the realized variance. --

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Bibliographic Info

Article provided by Springer in its journal Finance and Stochastics.

Volume (Year): 13 (2009)
Issue (Month): 2 (April)
Pages: 239-268

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Handle: RePEc:spr:finsto:v:13:y:2009:i:2:p:239-268

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Related research

Keywords: Bias correction; Integrated variance; Market microstructure noise; Realized range-based variance; Realized variance; 62E20; 62P20; C10; C80;

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References

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Cited by:
  1. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, Elsevier, vol. 26(C), pages 535-551.
  2. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
  3. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, Elsevier, vol. 18(4), pages 711-727, September.
  4. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, School of Economics and Management, University of Aarhus.
  5. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, Elsevier, vol. 161(2), pages 262-283, April.
  6. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, Elsevier, vol. 36(C), pages 191-210.

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