  Tail risk measures are fully determined by the distribution of the underlying
loss beyond its quantile at a certain level, with Value-at-Risk and Expected
Shortfall being prime examples. They are induced by law-based risk measures,
called their generators, evaluated on the tail distribution. This paper
establishes joint identifiability and elicitability results of tail risk
measures together with the corresponding quantile, provided that their
generators are identifiable and elicitable, respectively. As an example, we
establish the joint identifiability and elicitability of the tail expectile
together with the quantile. The corresponding consistent scores constitute a
novel class of weighted scores, nesting the known class of scores of Fissler
and Ziegel for the Expected Shortfall together with the quantile. For
statistical purposes, our results pave the way to easier model fitting for tail
risk measures via regression and the generalized method of moments, but also
model comparison and model validation in terms of established backtesting
procedures.
