Aristidis K. Nikoloulopoulos

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Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its(More)
In Aas et al. (2009) and Aas and Berg (2009), it is shown that vine copulas constructed from bivariate t-copulas can provide better fits to multivariate financial asset return data. Several published articles indicate that for several assets there might be stronger tail dependence of returns in the joint lower tail than upper tail. We use vine copula models(More)
Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at(More)
Applications of copulas for multivariate continuous data abound but there are only a few that treat multivariate binary data. In the present paper, we model multivariate binary data based on copulas using mixtures of max-infinitely divisible copulas, introduced by Joe and Hu (J. Multivar. Anal. 1996; 57(2): 240-265). When applying copulas to binary data the(More)
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