A geometric investigation into the tail dependence of vine copulas

Vine copulas are a type of multivariate dependence model, composed collection bivariate that combined according to specific underlying graphical structure. Their flexibility and practicality in moderate high dimensions have contributed the popularity vine copulas, but relatively little attention has been paid their extremal properties. To address this issue, we present results on tail properties some most widely studied copula classes. We focus our study coefficient asymptotic shape sample cloud, which calculate using geometric approach Nolde (2014). offer new insights by presenting for trivariate constructed from asymptotically dependent independent focusing extreme value inverted with additional detail provided logistic examples. also theory class higher dimensional copulas.