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The comonotonicity and countermonotonicity provide intuitive upper and lower depen-dence relationship between random variables. This paper constructs the shuffle of min’s ran-domvariableapproximations...
The comonotonicity and countermonotonicity provide intuitive upper and lower depen-dence relationship between random variables. This paper constructs the shuffle of min’s ran-domvariableapproximations...
For fitting a parametric copula to multivariate data, a popular way is to employ the so-called pseudo maximum likelihood estimation proposed by Genest, Ghoudi and Rivest (1995). Although interval esti...
Bivariate Fréchet (BF) copulas characterize dependence as a mixture of three simple structures: comonotonicity, in-dependence and countermonotonicity. They are easily interpretable but have limitation...
Levy copulas are the most natural concept to capture jump dependence in multivariate Levy processes. They translate the intuition and many features of the copula concept into a time series setting. A ...
The performance of known and new parametric estimators for Archimedean copulas is investigated, with special focus on large dimensions. In particular,method-of-moments-like estimators based on pairwis...
Extreme-value copulas arise in the asymptotic theory for componentwise maxima of independent random samples. An extreme-value copula is determined by its Pickands dependence function, which is a funct...
Starting from the characterization of extreme-value copulas based on maxstability, large-sample tests of extreme-value dependence for multivariate copulas are studied. The two key ingredients of the...
Building higher-dimensional copulas is generally recognized as a difficult problem. Regular-vines using bivariate copulas provide a flexible class of high-dimensional dependency models. In large dim...
It is often reasonable to assume that the dependence structure of a bivariate continuous distribution belongs to the class of extreme-value copulas. The latter are characterized by their Pickands depe...
In the problem of estimating the lower and upper tail copula we propose two bootstrap procedures for approximating the distribution of the corresponding empirical tail copulas. The first method ...
Some Properties of Schur-Constant Survival Models and their Copulas
On Grade Transformation and its Implications for Copulas
Consider a continuous random pair (X,Y ) whose dependence is characterized by an extreme-value copula with Pickands dependence function A. When the marginal distributions of X and Y are known, seve...

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