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Identifying multiple outliers in heavy-tailed distributions with an application to market crashes

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  • Schluter, Christian
  • Trede, Mark

Abstract

Heavy-tailed distributions, such as the distribution of stock returns, are prone to generate large values. This renders difficult the detection of outliers. We propose a new outward testing procedure to identify multiple outliers in these distributions. A major virtue of the test is its simplicity. The performance of the test is investigated in several simulation studies. As a substantive empirical contribution we apply the test to Dow Jones Industrial Average return data and find that the Black Monday market crash was not a structurally unusual event.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:empfin:v:15:y:2008:i:4:p:700-713
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    References listed on IDEAS

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    1. McCulloch, J Huston, 1997. "Measuring Tail Thickness to Estimate the Stable Index Alpha: A Critique," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 74-81, January.
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    3. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-663, May.
    4. Schluter, Christian & Trede, Mark, 2002. "Tails of Lorenz curves," Journal of Econometrics, Elsevier, vol. 109(1), pages 151-166, July.
    5. Klass, Oren S. & Biham, Ofer & Levy, Moshe & Malcai, Ofer & Solomon, Sorin, 2006. "The Forbes 400 and the Pareto wealth distribution," Economics Letters, Elsevier, vol. 90(2), pages 290-295, February.
    6. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    7. Novak, S.Y. & Beirlant, J., 2006. "The magnitude of a market crash can be predicted," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 453-462, February.
    8. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-216, April.
    9. Jesus Gonzalo, 2004. "Which Extreme Values Are Really Extreme?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(3), pages 349-369.
    10. Vandewalle, B. & Beirlant, J. & Christmann, A. & Hubert, M., 2007. "A robust estimator for the tail index of Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6252-6268, August.
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    Cited by:

    1. Talpsepp, Tõnn & Rieger, Marc Oliver, 2010. "Explaining asymmetric volatility around the world," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 938-956, December.
    2. Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
    3. Brée, David S. & Joseph, Nathan Lael, 2013. "Testing for financial crashes using the Log Periodic Power Law model," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 287-297.

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