An Algorithmic Inference Approach to Learn Copulas

Bruno Apolloni

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arXiv · 1910.02678

An Algorithmic Inference Approach to Learn Copulas

Bruno Apolloni

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Abstract

We introduce a new method for estimating the parameter of the bivariate Clayton copulas within the framework of Algorithmic Inference. The method consists of a variant of the standard boot-strapping procedure for inferring random parameters, which we expressly devise to bypass the two pitfalls of this specific instance: the non independence of the Kendall statistics, customarily at the basis of this inference task, and the absence of a sufficient statistic w.r.t. \alpha. The variant is rooted on a numerical procedure in order to find the \alpha estimate at a fixed point of an iterative routine. Although paired with the customary complexity of the program which computes them, numerical results show an outperforming accuracy of the estimates.

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Bruno Apolloni. 2019-10-07. An Algorithmic Inference Approach to Learn Copulas. https://arxiv.org/abs/1910.02678

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