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This paper discusses an approach to the correlation problem in which losses from different lines of insurance are linked by a common variation (or shock) in the parameters of each line’s loss model. The paper begins with a simple common shock model and graphically illustrates the effect of the magnitude of the shocks on correlation.
Although the copula literature has many instances of bivariate copulas, once more than two variates are correlated, the choice of copulas often comes down to selection of the degrees-of-freedom parameter in the t-copula. In search for a wider selection of multivariate copulas we review a generalization of the t-copula and some copulas defined by Harry Joe. Generalizing the t-copula gives more flexibility in setting tail behavior.
