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September 2013 question 7

Z

zuglubuglu

Member
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.
 
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