Copulas are important models that allow to capture the dependence among variables. There are many types of bivariate parametric copula families, which allow to model data sets with different properties: symmetric and asymmetric dependence, upper (lower) tail dependence. In higher dimensions popular families of copulas, e.g., Gaussian, Student-t and canonical Archimedean are not sufficiently flexible in representing different types of dependence that they can realize. By decomposing the multivariate copula into a sequence of bivariate (conditional) copulas, based on a graph called vine (which is a nested set of trees), one is able to construct a n dimensional copula with the bivariate copulas that can have different types of dependence (e.g., tail behavior and asymmetries). The model constructed this way is called the vine copulamodel...
|Award date||6 Jul 2022|
|Publication status||Published - 2022|