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Data-based ranking of realised volatility estimators

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  • Patton, Andrew J.

Abstract

This paper presents new methods for comparing the accuracy of estimators of the quadratic variation of a price process. I provide conditions under which the relative accuracy of competing estimators can be consistently estimated (as T-->[infinity]), and show that forecast evaluation tests may be adapted to the problem of ranking these estimators. The proposed methods avoid making specific assumptions about microstructure noise, and facilitate comparisons of estimators that would be difficult using methods from the extant literature, such as those based on different sampling schemes. An application to high frequency IBM data between 1996 and 2007 illustrates the new methods.

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  • Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
  • Handle: RePEc:eee:econom:v:161:y:2011:i:2:p:284-303
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    Cited by:

    1. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    2. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    3. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    4. Jianqing Fan & Jingjin Zhang & Ke Yu, 2008. "Asset Allocation and Risk Assessment with Gross Exposure Constraints for Vast Portfolios," Papers 0812.2604, arXiv.org.
    5. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    6. repec:eee:intfor:v:34:y:2018:i:2:p:276-287 is not listed on IDEAS
    7. Giorgio Mirone, 2906. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    8. repec:eee:finlet:v:25:y:2018:i:c:p:222-229 is not listed on IDEAS
    9. Simon Clinet & Yoann Potiron, 2017. "Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book," Papers 1709.02502, arXiv.org, revised Jun 2018.
    10. Umberto Triacca & Fulvia Focker, 2014. "Estimating overnight volatility of asset returns by using the generalized dynamic factor model approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 235-254, October.
    11. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
    12. repec:kap:apfinm:v:24:y:2017:i:3:d:10.1007_s10690-017-9228-z is not listed on IDEAS
    13. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    14. repec:eee:intfor:v:33:y:2017:i:4:p:1105-1123 is not listed on IDEAS

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