and techniques such as copulas. …

copulas, which allow us to model the effect of a covariate driving the strength of dependence between the main variables. We … propose a flexible Bayesian nonparametric approach for the estimation of conditional copulas, which can model any conditional …

consider a novel family of bivariate copulas, called exchangeable Marshall copulas. Such copulas describe both positive and … copulas are introduced, based on the estimation of their (univariate) generator. Moreover, the performance of the proposed …

Extreme-value copulas arise as the possible limits of copulas of component-wise maxima of independent, identically … distributed samples. The use of bivariate extreme-value copulas is greatly facilitated by their representation in terms of …

models are characterized by nonparametric invariant distributions and parametric copula functions; where the copulas capture … copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes … generated via Clayton, Gumbel and Student’s t copulas (with tail dependence) are all geometric ergodic. We then propose a sieve …

functions; where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated … via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first … show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all …