  In risk management, tail risks are of crucial importance. The assessment of
risks should be carried out in accordance with the regulatory authority's
requirement at high quantiles. In general, the underlying distribution function
is unknown, the database is sparse, and therefore special tail models are used.
Very often, the generalized Pareto distribution is employed as a basic model,
and its parameters are determined with data from the tail area. With the
determined tail model, statisticians then calculate the required high
quantiles. In this context, we consider the possible accuracy of the
calculation of the quantiles and determine the finite sample distribution
function of the quantile estimator, depending on the confidence level and the
parameters of the tail model, and then calculate the finite sample bias and the
finite sample variance of the quantile estimator. Finally, we present an impact
analysis on the quantiles of an unknown distribution function.
