  Threshold selection plays a key role for various aspects of statistical
inference of rare events. Most classical approaches tackling this problem for
heavy-tailed distributions crucially depend on tuning parameters or critical
values to be chosen by the practitioner. To simplify the use of automated,
data-driven threshold selection methods, we introduce two new procedures not
requiring the manual choice of any parameters. The first method measures the
deviation of the log-spacings from the exponential distribution and achieves
good performance in simulations for estimating high quantiles. The second
approach smoothly estimates the asymptotic mean square error of the Hill
estimator and performs consistently well over a wide range of distributions.
The methods are compared to existing procedures in an extensive simulation
study and applied to a dataset of financial losses, where the underlying
extreme value index is assumed to vary over time. This application strongly
emphasizes the importance of solid automated threshold selection.
