Abstract
Modeling dependence between random variables is accomplished effectively by using copula functions. Practitioners often rely on the single parameter Archimedean family which contains a large number of functions, exhibiting a variety of dependence structures. In this work we propose the use of the multiple-parameter compound Archimedean family, which extends the original family and allows more elaborate dependence structures. In particular, we use a copula of this type to model the dependence structure between the minimum daily electricity demand and the maximum daily temperature. It is shown that the compound Archimedean copula enhances the flexibility of the dependence structure and provides a better
 fit to the data.
Highlights
Modeling the dependence structure between random variables is a long-standing research interest
In this work we propose the use of the multiple-parameter compound Archimedean family, which extends the original family and allows more elaborate dependence structures
We investigate the application of a compound Archimedean copula in electricity demand
Summary
Modeling the dependence structure between random variables is a long-standing research interest. The key characteristic of this concept is that both of the random variables have the same parametric univariate distribution This restriction does not exist when using copula functions (Genest and Favre, (2007)). Numerous other studies have investigated electricity peak demand, and used copula functions to research the dependence structures between components which affect it. Research on minimum electricity demand has received less attention, and even less so when it comes to dependence structures using copula functions.
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