Linear B-spline copulas with applications to nonparametric estimation of copulas

Xiaojing Shen, Yunmin Zhu and Lixin Song

Computational Statistics & Data Analysis, 2008, vol. 52, issue 7, 3806-3819

Abstract: In this paper, we propose a method for constructing a new class of copulas. They are called linear B-spline copulas which are a good approximation of a given complicated copula by using finite numbers of values of this copula without the loss of some essential properties. Moreover, rigorous analysis shows that the empirical linear B-spline copulas are more effective than empirical copulas to estimate perfectly dependent copulas. For the cases of nonperfectly dependent copulas, simulations show that the empirical linear B-spline copulas also improve the empirical copulas to estimate the underlying copula structure. Furthermore, we compare the performance of parametric estimation of copulas based on the empirical copulas with that based on the empirical linear B-spline copulas by simulations. In most of the cases, the latter are better than the former.

Date: 2008
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Citations: View citations in EconPapers (16)

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