  In risk management, tail risks are of crucial importance. The quality of a
tail model, which is determined by data from an unknown distribution, depends
critically on the subset of data used to model the tail. Based on a suitably
weighted mean square error, we present a method that can separate the required
subset. The selected data are used to determine the parameters of the tail
model. Notably, no parameter specifications have to be made to apply the
proposed procedure. Standard goodness of fit tests allow us to evaluate the
quality of the fitted tail model. We apply the method to standard distributions
that are usually considered in the finance and insurance industries. In
addition, for the MSCI World Index, we use historical data to identify the tail
model and to compute the quantiles required for a risk assessment.
