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Outlyingness Weighted Covariation

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  • Christophe Croux
  • Sébastien Laurent

Abstract

Quadratic covariation is a popular descriptive measure for the volatility of a multivariate price process. It is consistently estimated by the sum of outer products of high-frequency returns. The proposed realized outlyingness weighted covariation (ROWCov) is a weighted sum of outer products of high-frequency returns and downweights returns that, because of jumps or other reasons, are outliers under the Brownian semimartingale model. The ROWCov is positive semidefinite and remains consistent for the integrated covariance in the presence of a finite-activity jump process. We illustrate the usefulness of the estimator on five-minute returns on the transaction prices of the Dow Jones Industrial Average constituents. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Christophe Croux & Sébastien Laurent, 2011. "Outlyingness Weighted Covariation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(4), pages 657-684.
  • Handle: RePEc:oup:jfinec:v:9:y:2011:i:4:p:657-684
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbr003
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    Cited by:

    1. Manabu Asai & Michael McAleer, 2017. "The impact of jumps and leverage in forecasting covolatility," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 638-650, October.
    2. Iulia LUPU & Gheorghe HURDUZEU & Mariana NICOLAE, 2016. "Connections Between Sentiment Indices And Reduced Volatilities Of Sustainability Stock Market Indices," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 157-174.
    3. Veredas, David & Vander Elst, Harry, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    5. Chan, Kam Fong & Bowman, Robert G. & Neely, Christopher J., 2017. "Systematic cojumps, market component portfolios and scheduled macroeconomic announcements," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 43-58.
    6. Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
    7. repec:eee:intfor:v:34:y:2018:i:1:p:45-63 is not listed on IDEAS
    8. repec:eee:intfor:v:33:y:2017:i:3:p:729-742 is not listed on IDEAS
    9. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    10. Gnabo, Jean-Yves & Hvozdyk, Lyudmyla & Lahaye, Jérôme, 2014. "System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 147-174.
    11. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    12. repec:cte:wsrepe:es142416 is not listed on IDEAS

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