  We propose an approach to the aggregation of risks which is based on
estimation of simple quantities (such as covariances) associated to a vector of
dependent random variables, and which avoids the use of parametric families of
copulae. Our main result demonstrates that the method leads to bounds on the
worst case Value at Risk for a sum of dependent random variables. Its proof
applies duality theory for infinite dimensional linear programs.
