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Extreme Value Theory Filtering Techniques for Outlier Detection

  • Olmo, J.

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.

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File URL: http://openaccess.city.ac.uk/1581/1/0909_olmo.pdf
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Paper provided by Department of Economics, City University London in its series Working Papers with number 09/09.

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Date of creation: 2009
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Handle: RePEc:cty:dpaper:09/09
Contact details of provider: Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
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  1. Jesus Gonzalo, 2004. "Which Extreme Values Are Really Extreme?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(3), pages 349-369.
  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. 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.
  4. 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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