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摘要
The selection of copulas is an important aspect of dependence modeling. In many practical applications, only a limited number of copulas is tested, and the modeling applications usually are restricted to the bivariate case. One explanation is the fact that no graphical copula tool exists that allows us to assess the goodness-of-fit of a large set of (possible higher-dimensional) copula functions at once. This article seeks to overcome this problem by developing a new graphical tool for the copula selection, based on a statistical analysis technique called %26quot;principal coordinate analysis.%26quot; The advantage is three-fold. First, when projecting the empirical copula of a modeling application on a two-dimensional (2D) copula space, it allows us to visualize the fit of a whole collection of multivariate copulas at once. Second, the visual tool allows us to identify %26quot;search%26quot; directions for potential fit improvements (e.g., through the use of copula transforms). Finally, the tool makes it also possible to give a 20 visual overview of a large number of known copula families, leading to a better understanding and a more efficient use of the different copula families. The robustness of the new graphical tool is investigated by means of a small simulation study, and the practical use of the tool is demonstrated for two 2D and two 3D (three-dimensional) fitting examples. MATLAB code through the examples is available online in the supplementary materials.
- 出版日期2013-6
