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• W3104441629 abstract "In risk management, tail risks are of crucial importance. The assessment of risks should be carried out in accordance with the regulatory authority's requirement at high quantiles. In general, the underlying distribution function is unknown, the database is sparse, and therefore special tail models are used. Very often, the generalized Pareto distribution is employed as a basic model, and its parameters are determined with data from the tail area. With the determined tail model, statisticians then calculate the required high quantiles. In this context, we consider the possible accuracy of the calculation of the quantiles and determine the finite sample distribution function of the quantile estimator, depending on the confidence level and the parameters of the tail model, and then calculate the finite sample bias and the finite sample variance of the quantile estimator. Finally, we present an impact analysis on the quantiles of an unknown distribution function." @default.
• W3104441629 created "2020-11-23" @default.
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• W3104441629 date "2020-06-27" @default.
• W3104441629 modified "2023-10-03" @default.
• W3104441629 title "Tail models and the statistical limit of accuracy in risk assessment" @default.
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• W3104441629 doi "https://doi.org/10.1108/jrf-11-2019-0217" @default.
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