Quantifying Correlated Reinsurance Exposures with Copulas

Copulas provide a convenient way to represent joing distributions. In fact the joing distribution function can be expressed as the copula function applied to the separate individual distributions. In fact the joing distribution function can be expressed as the copula function applied to the separate individual distributions. That is [see article for formula] where C is the copula function. Background information on copulas is covered in my Proceedings paper "Tails of Copulas" as will be largely assumed here. This paper focuses on the t-copula, which meets these minimum requirements, but just barely. You can input a correlation matrix and you do have control over the tail behavior, but you only have one parameter to control the tail, so all pairs of variates will have tain correlation that is determined by that parameter. The normal copula is a limiting case, in which the tails are ultimately uncorrelated if you go out far enough.