Robust risk aggregation with neural networks

Understand

We consider settings in which the distribution of a multivariate random variable is partly ambiguous.

• We assume the ambiguity lies on the level of the dependence structure, and that the marginal distributions are known.
• Furthermore, a current best guess for the distribution, called reference measure, is available.
• We work with the set of distributions that are both close to the given reference measure in a transportation distance (e.g.