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Some Convergence Problems On Heavy Tail Estimation Using Upper Order Statistics For Generalized Pareto and Lognormal Distributions

Listed author(s):
  • Hernandez-Molinar, Raul
  • Lefante, John
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    In some applications, the population characteristics of main interest can be found in the tails of the distribution function. The study of risk of extreme events will lead to the use of probability distributions and the scenarios that correspond to the tail of these distributions. Considering two approaches: parametric and nonparametric, the research emphasizes the assessment of distribution tails, assuming that underlying distributions are heavy tailed. Two heavy tailed distributions are considered: Generalized Pareto and Lognormal. The Maximum likelihood estimation method, using the complete sample, and using only the upper order statistics provide estimators of the parameters. Measures of Bias and Mean Squared Error of the estimators of the parameters, and the Conditional Mean Exceedence Functions of the distributions, are generated. The methodology for estimating population parameters, has potential applications in financial markets, quality control, assurance portfolios, monitoring of residual discharges, medical applications, design of environmental policies, or calibration and adjustment of processes and equipment. The main idea is to present, and analyze the methods used for the estimation, and some convergence problems when these two distribution functions are used in generating scenarios.

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    Paper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2003,30.

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    Date of creation: 2003
    Handle: RePEc:zbw:sfb373:200330
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