Hidden regular variation, copula models, and the limit behavior of conditional excess risk measures

Abstract

Risk measures like Marginal Expected Shortfall and Marginal Mean Excess quantify conditional risk and in particular, aid in the understanding of systemic risk. In many such scenarios, models exhibiting heavy tails in the margins and asymptotic tail independence in the joint behavior play a fundamental role. The notion of hidden regular variation has the advantage that it models both properties: asymptotic tail independence as well as heavy tails. An alternative approach to addressing these features is via copulas. First, we elicit connections between hidden regular variation and the behavior of tail copula parameters extending previous works in this area. Then we study the asymptotic behavior of the aforementioned conditional excess risk measures; first under hidden regular variation and then under restrictions on the tail copula parameters, not necessarily assuming hidden regular variation. We provide a broad variety of examples of models admitting heavy tails and asymptotic tail independence along with hidden regular variation and with the appropriate limit behavior for the risk measures of interest.

Approximation of some multivariate risk measures for Gaussian risks

“…Indeed that phenomenon renders the approximations of both MME and MES interesting and challenging. Under hidden regular variation assumption on (Z 1 , Z 2 ) the recent publications [1,5] consider approximations of MME and MES under some additional asymptotic conditions. However the Gaussian setup is not covered therein since the marginal distributions are in our setup light-tailed.…”