  Uncertainty requires suitable techniques for risk assessment. Combining
stochastic approximation and stochastic average approximation, we propose an
efficient algorithm to compute the worst case average value at risk in the face
of tail uncertainty. Dependence is modelled by the distorted mix method that
flexibly assigns different copulas to different regions of multivariate
distributions. We illustrate the application of our approach in the context of
financial markets and cyber risk.
