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.
Original language | English |
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Pages (from-to) | 20-26 |
Number of pages | 7 |
Journal | International Journal of Statistics and Probability |
Volume | 10 |
Issue number | 5 |
DOIs | |
State | Published - Sep 2021 |