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Bayesian inference for extreme quantiles of heavy tailed distributions

Author

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  • Farias, Rafael B.A.
  • Montoril, Michel H.
  • Andrade, José A.A.

Abstract

We propose a new method for estimating extremes quantiles of a wide class of heavy-tailed distributions. Our proposal makes Bayesian inference on extreme quantiles through High Posterior Density intervals. We evaluate the performance of the proposal by numerical results.

Suggested Citation

  • Farias, Rafael B.A. & Montoril, Michel H. & Andrade, José A.A., 2016. "Bayesian inference for extreme quantiles of heavy tailed distributions," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 103-107.
    • Handle: RePEc:eee:stapro:v:113:y:2016:i:c:p:103-107
    DOI: 10.1016/j.spl.2016.02.020
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    References listed on IDEAS

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    1. 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.
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    3. Ahn, Soohan & Kim, Joseph H.T. & Ramaswami, Vaidyanathan, 2012. "A new class of models for heavy tailed distributions in finance and insurance risk," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 43-52.
    4. 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.
    5. Stuart G. Coles & Jonathan A. Tawn, 1996. "A Bayesian Analysis of Extreme Rainfall Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 463-478, December.
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    Cited by:

    1. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).

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