Parametric Estimation Procedures in Multivariate Generalized Pareto Models

Modelling the tails of a multivariate distribution can be reasonably done by multivariate generalized Pareto distributions (GPDs). We present several methods of parametric estimation in these models, which use decompositions of the corresponding random vectors with the help of different versions of Pickands coordinates. The estimators are compared to each other with simulated data sets. To show the practical value of the methods, they are applied to a real hydrological data set.