Copulas are functions that enable the construction of multivariate probability distributions by binding together univariate marginal distributions. Central to probability theory, they allow ...

For the problem of estimating lower tail and upper tail copulas, we propose two bootstrap procedures for approximating the distribution of the corresponding empirical tail copulas. The first method ...

We build a class of copula models that captures time-varying dependence across large panels of financial assets. Our models nest Gaussian, Student's t, grouped Student's t, and generalized hyperbolic ...

For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the ...