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Estimating quadratic variation when quoted prices change by a constant increment

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  • Large, Jeremy

Abstract

For financial assets whose best quotes almost always change by jumping by the market's price tick size (one cent, five cents, etc.), this paper proposes an estimator of Quadratic Variation which controls for microstructure effects. It measures the prevalence of alternations, where quotes jump back to their just-previous price. It defines a simple property called "uncorrelated alternation", which under conditions implies that the estimator is consistent in an asymptotic limit theory, where jumps become very frequent and small. Feasible limit theory is developed, and in simulations works well.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 160 (2011)
Issue (Month): 1 (January)
Pages: 2-11

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Handle: RePEc:eee:econom:v:160:y:2011:i:1:p:2-11

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Realized volatility Realized variance Quadratic variation Market microstructure High-frequency data Pure jump process;

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Citations

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Cited by:
  1. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
  2. Kalnina, Ilze & Linton, Oliver, 2008. "Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error," Journal of Econometrics, Elsevier, vol. 147(1), pages 47-59, November.
  3. Christensen, Kim & Podolskij, Mark & Vetter, Mathias, 2006. "Bias-Correcting the Realized Range-Based Variance in the Presence of Market Microstructure Noise," Technical Reports 2006,52, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  4. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
  5. Yingying Li & Per A. Mykland, 2007. "Are volatility estimators robust with respect to modeling assumptions?," Papers 0709.0440, arXiv.org.

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