Journal of Data Science

In the form of a scholarly exchange with ChatGPT, we cover fundamentals of modeling stochastic dependence with copulas. The conversation is aimed at a broad audience and provides a light introduction to the topic of copula modeling, a field of potential relevance in all areas where more than one random variable appears in the modeling process. Topics covered include the definition, Sklar’s theorem, the invariance principle, pseudo-observations, tail dependence and stochastic representations. The conversation also shows to what degree it can be useful (or not) to learn about such concepts by interacting with the current version of a chatbot.

In this paper, we advance new families of bivariate copulas constructed by distributional distortions of existing bivariate copulas. The distortions under consideration are based on the unit gamma distribution of two forms. When the initial copula is Archimedean, the induced copula is also Archimedean under the admissible parameter space. Properties such as Kendall’s tau coefficient, tail dependence coefficients and tail orders for the new families of copulas are derived. An empirical application to economic indicator data is presented.