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.
The present paper addresses computational and numerical challenges when working with t copulas and their more complicated extensions, the grouped t and skew t copulas. We demonstrate how the R package nvmix can be used to work with these copulas. In particular, we discuss (quasi-)random sampling and fitting. We highlight the difficulties arising from using more complicated models, such as the lack of availability of a joint density function or the lack of an analytical form of the marginal quantile functions, and give possible solutions along with future research ideas.