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Adaptive Models for Tail of Distributions

O.S Adesina, D.A Agunbiade, O. Ibhadode


Modelling tails of distributions can performed with models such as Weibull, Frechet and Gumbel distribution, it can also be modelled with generalized extreme value (GEV); a model which combines the three models. The limitation of GEV model is that it is not sufficient to model some levels of fat tail distributions. To identify the strength of GEV, Simulation study was carried out using both Generalized Pareto distributions (GPD) and GEV models with maximum likelihood estimate (MLE), and comparison was drawn between the models. Empirical results revealed that GPD model is sufficient to model fat tail distributions well irrespective of the data points, On the other hand, GEV models thin tail distribution better than GPD. GPD was modelled using electrical energy dataset and obtained tail risk, while GEV was used to model daily foreign exchange data.


Extreme Value Theory, tail risk, Peak Over Threshold, foreign exchange, electrical energy

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