  When estimating the risk of a P&L from historical data or Monte Carlo
simulation, the robustness of the estimate is important. We argue here that
Hampel's classical notion of qualitative robustness is not suitable for risk
measurement and we propose and analyze a refined notion of robustness that
applies to tail-dependent law-invariant convex risk measures on Orlicz space.
This concept of robustness captures the tradeoff between robustness and
sensitivity and can be quantified by an index of qualitative robustness. By
means of this index, we can compare various risk measures, such as distortion
risk measures, in regard to their degree of robustness. Our analysis also
yields results that are of independent interest such as continuity properties
and consistency of estimators for risk measures, or a Skorohod representation
theorem for {\psi}-weak convergence.
