Introduction

Vine copulas are fairly abstract objects that allow for flexible modeling of multivariate probability distributions. One of the main advantages of vine copulas, as will be shown in this module, is that they facilitate the representation of important possible asymmetries in multivariate distributions that other models do not. One of the main disadvantages is that they are somewhat more abstract than other kinds of models; hence, some degree of mathematical notions is required for their treatment.

This section introduces, somewhat informally, the concept of a vine copula. For a more formal treatment, refer to Kurowicka and Joe (2011)[1] and Czado (2019)[2].

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[1]

Kurowicka, D. and Joe H. (2011). Dependence Modeling: Vine Copula Handbook. World Scientific, Hackensack, NJ.

[2]

Czado, C. (2019). Analyzing dependent data with vine copulas: A practical guide with R, Vol. 222. https://doi.org/10.1007/978-3-030-13785-4_1