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Extreme Value Theory Filtering Techniques for Outlier Detection

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  • Olmo, J.

Abstract

We introduce asymptotic parameter-free hypothesis tests based on extreme value theory to detect outlying observations in finite samples. Our tests have nontrivial power for detecting outliers for general forms of the parent distribution and can be implemented when this is unknown and needs to be estimated. Using these techniques this article also develops an algorithm to uncover outliers masked by the presence of influential observations.

Suggested Citation

  • Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:09/09
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    File URL: https://openaccess.city.ac.uk/id/eprint/1581/1/0909_olmo.pdf
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    References listed on IDEAS

    as
    1. Armelle Guillou & Peter Hall, 2001. "A diagnostic for selecting the threshold in extreme value analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 293-305.
    2. Schluter, Christian & Trede, Mark, 2008. "Identifying multiple outliers in heavy-tailed distributions with an application to market crashes," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 700-713, September.
    3. Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Tinbergen Institute Discussion Papers 05-092/4, Tinbergen Institute.
    4. Jesus Gonzalo, 2004. "Which Extreme Values Are Really Extreme?," Journal of Financial Econometrics, Oxford University Press, vol. 2(3), pages 349-369.
    5. Basmann, Robert L., 2003. "Statistical outlier analysis in litigation support: the case of Paul F. Engler and Cactus Feeders, Inc., v. Oprah Winfrey et al," Journal of Econometrics, Elsevier, vol. 113(1), pages 159-200, March.
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