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A Pareto Tail Plot Without Moment Restrictions

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  • Bernhard Klar

Abstract

We propose a mean functional that exists for arbitrary probability distributions and characterizes the Pareto distribution within the set of distributions with finite left endpoint. This is in sharp contrast to the mean excess plot, which is meaningless for distributions without an existing mean and has nonstandard behavior when the mean is finite, but the second moment does not exist. The construction of the plot is based on the principle of a single huge jump, which differentiates between distributions with moderately heavy and super heavy tails. We present an estimator of the tail function based on U-statistics and study its large sample properties. Several loss datasets illustrate the use of the new plot.

Suggested Citation

  • Bernhard Klar, 2025. "A Pareto Tail Plot Without Moment Restrictions," The American Statistician, Taylor & Francis Journals, vol. 79(2), pages 156-166, April.
  • Handle: RePEc:taf:amstat:v:79:y:2025:i:2:p:156-166
    DOI: 10.1080/00031305.2024.2413081
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    References listed on IDEAS

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    1. Lehtomaa, Jaakko, 2015. "Limiting behaviour of constrained sums of two variables and the principle of a single big jump," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 157-163.
    2. Holger Drees, 2012. "Extreme value analysis of actuarial risks: estimation and model validation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 225-264, June.
    3. Joseph P. Romano, 2004. "On Non‐parametric Testing, the Uniform Behaviour of the t‐test, and Related Problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(4), pages 567-584, December.
    4. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    5. Samuelson, Paul A., 1967. "General Proof that Diversification Pays*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 2(1), pages 1-13, March.
    6. Trapani, Lorenzo, 2016. "Testing for (in)finite moments," Journal of Econometrics, Elsevier, vol. 191(1), pages 57-68.
    7. Igor Fedotenkov, 2013. "A bootstrap method to test for the existence of finite moments," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 315-322, June.
    8. Resnick, Sidney I., 1997. "Discussion of the Danish Data on Large Fire Insurance Losses," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 139-151, May.
    9. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    10. Rizzo, Maria L., 2009. "New Goodness-of-Fit Tests for Pareto Distributions," ASTIN Bulletin, Cambridge University Press, vol. 39(2), pages 691-715, November.
    11. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
    12. Brazauskas, Vytaras & Serfling, Robert, 2003. "Favorable Estimators for Fitting Pareto Models: A Study Using Goodness-of-fit Measures with Actual Data," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 365-381, November.
    13. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
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