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Pair-copula constructions (or vine copulas) are structured, in the layout of vines, with bivariate copulas and conditional bivariate copulas. The main contribution of the current work is an approach to the long-standing problem: how to cope with the dependence structure between the two conditioned variables indicated by an edge, acknowledging that the dependence structure changes with the values of the conditioning variables. This problem is known as the non-simplified vine copula modelling and, though recognized as crucial in the field of multivariate modelling, remains widely unexplored due to its inherent complication, and hence is the motivation of the current work. Rather than resorting to traditional parametric or non-parametric methods, we proceed from an innovative viewpoint: approximating a conditional copula, to any required degree of approximation, by utilizing a family of basis functions. We fully incorporate the impact of the conditioning variables on the functional form of a conditional copula by employing local learning methods. The attractions and dilemmas of the pair-copula approximating technique are revealed via simulated data, and its practical importance is evidenced via a real data set

Vine copula approximation: a generic method for coping with conditional dependence