In part 2, the model solutions discuss how Aggregate insurance risk is greater than the sum of its part and therefore there must be an error. However isn't this assuming that a correlation method is being used? A copula with heavy tail correlation might actually result in an aggregate insurance risk larger than the sum of parts (plus VaR is not sub-additive). Am I missing something here?
I think that perfect positive correlation would result in all percentiles being additive, and thus creates an upper bound. Any correlation less than this would only reduce the aggregated loss at a particular percentile.
True but it is assuming that a correlation method is assumed (like Solvency II standard model). This is probably a VaR and VaR isn't subadditive.
