This thesis reviews recent methods employed for the simulation of Elliptical and Archimedean copulas and provides guidance for the implementation and testing of such simulations. The ultimate purpose of copula simulations is the generation of samples from a multivariate (joint) distribution, through the Monte Carlo sampling method. Simulations of such multivariate
distributions have proven to be useful, when the number of marginals contained in a multivariate distribution is high, and analytic approaches get highly expensive in terms
of computational effort or do not even exist. A second important advantage of copulas is their ability to decouple the dependence structure and marginals from one another, such that those can be modeled separately. Copulas are prominently used in quantitative finance, but have found applications in various other fields such as climate and weather research, civil engineering and medical research.
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This thesis reviews recent methods employed for the simulation of Elliptical and Archimedean copulas and provides guidance for the implementation and testing of such simulations. The ultimate purpose of copula simulations is the generation of samples from a multivariate (joint) distribution, through the Monte Carlo sampling method. Simulations of such multivariate
distributions have proven to be useful, when the number of marginals contained in a multivariate distribution is high, and analytic...
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