On the basis of order statistics, Baker (2008) proposed a method for constructing multivariate distributions with fixed marginals. This is another representation of the Bernstein copula. According to the construction of Baker’s distribution, the Bernstein copula can be regarded as a finite mixture distribution. In this paper, we propose expectationmaximization (EM) algorithms to estimate the Be...

background: in survival studies when the event times are dependent, performing of the analysis by using of methods based on independent assumption, leads to biased. in this paper, using copula function and considering the dependence structure between the event times, a parametric joint distribution has made fitting to the events, and the effective factors on each of these events would be determ...

A notion of higher order tail densities for copulas is introduced using multivariate regular variation of copula densities, and densities of multivariate extremes with various margins can then be studied in a unified fashion. We show that the tail of a multivariate density can be decomposed into the tail density of the underlying copula, coupled with marginal tail transforms of the three types:...

In this paper we propose a copula-based technique to recover the distribution of actively managed funds. The copula is meant to represent the dependence structure between the market return (or in general the benchmark) and the investment strategy of the asset manager. The analysis is carried out in a rational investor economy with managed funds, such as that in Merton (1981) and Berk and Green ...

We describe the R package kdecopula (current version 0.9.0), which provides fast implementations of various kernel estimators for the copula density. Due to a variety of available plotting options it is particularly useful for the exploratory analysis of dependence structures. It can be further used for accurate nonparametric estimation of copula densities and resampling. The implementation fea...

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-GumbelMorgenstern) FGM bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions b...

We propose a new test for the hypothesis that a bivariate copula is an Archimedean copula. The test statistic is based on a combination of two measures resulting from the characterization of Archimedean copulas by the property of associativity and by a strict upper bound on the diagonal by the Fréchet-upper bound. We prove weak convergence of this statistic and show that the critical values of ...

This paper suggests to use Lévy copulas to characterize the dependence among components of multidimensional Lévy processes. This concept parallels the notion of a copula on the level of Lévy measures. As for random vectors, a kind of Sklar’s theorem states that the law of a general multivariate Lévy process is obtained by combining arbitrary univariate Lévy processes with an arbitrary Lévy copu...

A continuous random vector (X,Y) uniquely determines a copula C : [0,1]2 → [0,1] such that when the distribution functions of X and Y are properly composed into C , the joint distribution function of (X,Y) results. A copula is said to be D4-invariant if its mass distribution is invariant with respect to the symmetries of the unit square. A D4-invariant copula leads naturally to a family of meas...

In this paper we propose a Wavelet Pair Copula Construction approach for contagion identification. The method consists in filtering past marginal dependence, performing multiscale decomposition in marginal residuals, and estimating a Pair Copula Construction for each frequency scale of interest. We carry out these steps with daily data from U.S., German, Brazilian and Hong Kong MSCI indices. Th...