VineCopulas: an open-source Python package for vine copula modelling

Summary

A copula method can be used to describe the dependency structure between several random variables. Copula methods are used widely in various research fields across different disciplines, ranging from finance to the bio-geophysical sciences (Dißmann et al., 2013; Klein et al., 2020; Mitskopoulos et al., 2022). While some other multivariate distributions, for instance a multivariate normal distribution, allow for a highly symmetric dependency structure with the same univariate and multivariate marginal distributions, copulas can model the joint distribution of multiple random variables separately from their marginal distribution (Czado & Nagler, 2021; Sklar, 1959). Once a copula distribution has been modelled, they allow for random samples of the data to be generated, as well as conditional samples. For example, if a copula has been fit between people’s height and weight, this copula can create random correlated samples of both variables as well as conditional samples, e.g., samples of weight given a specific height.

Published: 11 September 2024