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The Generalized Pareto Distribution (GPD) plays a central role in modellingheavy tail phenomena in many applications. Applying the GPD to actual datasetshowever is a non-trivial task. One common way suggested in the literature toinvestigate the tail behaviour is to take logarithm to the original dataset inorder to reduce the sample variability. Inspired by this, we propose and studythe Exponentiated Generalized Pareto Distribution (exGPD), which is created vialog-transform of the GPD variable. After introducing the exGPD we derivevarious distributional quantities, including the moment generating function,tail risk measures. As an application we also develop a plot as an alternativeto the Hill plot to identify the tail index of heavy tailed datasets, based onthe moment matching for the exGPD. Various numerical analyses with bothsimulated and actual datasets show that the proposed plot works well.

Exponentiated Generalized Pareto Distribution: Properties and applications towards Extreme Value Theory