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Estimation of Truncated Data Samples in Operational Risk Modeling

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  • Bakhodir Ergashev
  • Konstantin Pavlikov
  • Stan Uryasev
  • Evangelos Sekeris

Abstract

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  • Bakhodir Ergashev & Konstantin Pavlikov & Stan Uryasev & Evangelos Sekeris, 2016. "Estimation of Truncated Data Samples in Operational Risk Modeling," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 613-640, September.
    • Handle: RePEc:bla:jrinsu:v:83:y:2016:i:3:p:613-640
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    File URL: http://hdl.handle.net/10.1111/jori.12062
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    References listed on IDEAS

    as
    1. Pavel V. Shevchenko & Grigory Temnov, 2009. "Modeling operational risk data reported above a time-varying threshold," Papers 0904.4075, arXiv.org, revised Jul 2009.
    2. Pavel V. Shevchenko, 2010. "Implementing loss distribution approach for operational risk," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 277-307, May.
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    Cited by:

    1. Daoping Yu & Vytaras Brazauskas, 2017. "Model Uncertainty in Operational Risk Modeling Due to Data Truncation: A Single Risk Case," Risks, MDPI, vol. 5(3), pages 1-17, September.
    2. J. D. Opdyke, 2016. "Fast, Accurate, Straightforward Extreme Quantiles of Compound Loss Distributions," Papers 1610.03718, arXiv.org, revised Jul 2017.
    3. Giuricich, Mario Nicoló & Burnecki, Krzysztof, 2019. "Modelling of left-truncated heavy-tailed data with application to catastrophe bond pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 498-513.
    4. Maria-Teresa Bosch-Badia & Joan Montllor-Serrats & Maria-Antonia Tarrazon-Rodon, 2020. "Risk Analysis through the Half-Normal Distribution," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    5. J. D. Opdyke, 2014. "Estimating Operational Risk Capital with Greater Accuracy, Precision, and Robustness," Papers 1406.0389, arXiv.org, revised Nov 2014.

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