Aggregation operators transform a finite number of inputs, called arguments, into a single output. They are applied in many theoretical and practical domains and in particular aggregation operators play important role in different approaches to decision making, where values to be aggregated are typically preference or satisfaction degrees. Many operators of different type have been considered i...

In recent years copulas turned out to be a promising tool in multivariate modelling, mostly with applications in actuarial sciences and hydrology. In short, copula is a function which allows modelling dependence structure between stochastic variables. The main advantage is that the copula approach can split the problem of constructing multivariate probability distributions into a part containin...

Multivariate exchangeable Archimedean copulas are one of the most popular classes of copulas that are used in actuarial science and finance for modelling risk dependencies and for using them to quantify the magnitude of tail dependence. Owing to the increase in popularity of copulas to measure dependent risks, generating multivariate copulas has become a very crucial exercise. Current methods f...

We propose an approach allowing to manage the statistical data. At same time, starting from copulas of Gumbel,of Ali-Michael and Haq., we some Archimedean family that estimate contribute resolution problems related choice maximum or optimal values for a group random variables united within d vectors size n, independent identifically distributed, suitably normalized as n tends infinity. To our k...

Liouville copulas, which were introduced in [27], are asymmetric generalizations of the ubiquitous Archimedean copula class. They are the dependence structures of scale mixtures of Dirichlet distributions, also called Liouville distributions. In this paper, the limiting extreme-value copulas of Liouville copulas and of their survival counterparts are derived. The limiting max-stable models, ter...

In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms w...

Purpose The authors apply their method to analyze which portfolios are capable of providing superior performance those based on the Sharpe ratio (SR). Design/methodology/approach In this paper illustrate use conditional copulas for identifying differences in alternative portfolio strategies. SR. Findings results show that under Gaussian copula, both expected tail (ETR) and skewness-kurtosis exh...

In econometrics, many distributions are non-Gaussian. To describe dependence between non-Gaussian variables, it is usually not sufficient to provide their correlation: it is desirable to also know the corresponding copula. There are many different families of copulas; which family shall we use? In many econometric applications, two families of copulas have been most efficient: the Clayton and t...