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Smooth Nonparametric Copula Estimation with Least Squares Support Vector Regression

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Abstract

Copula has become the standard tool in dependence modeling. With the aide of copula, the estimation of multivariate distributions can be obtained by two steps: marginal distributions construction and copula estimation. This paper puts forward a smooth nonparametric copula estimation based on least squares support vector regression. By supplementing the classical least squares support vector regression with some additional shape-related constraints, this method tries to make the estimator satisfy three shape restrictions of copula: grounded, marginal and 2-increasing. Its training involves a simple convex quadratic programming problem, which can be solved in polynomial time. Experimental results clearly showed that this method could achieve significantly better performance than the classical least squares support vector regression and kernel smoother for copula estimation.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (71101127), Social Sciences Foundation of Chinese Ministry of Education (10YJC790265) and Zhejiang Province Universities Social Sciences Key Base (Research Center for Finance of Zhejiang Gongshang University).

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  1. School of Finance, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China

    Yongqiao Wang

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  1. Yongqiao Wang
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Correspondence to Yongqiao Wang.

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Wang, Y. Smooth Nonparametric Copula Estimation with Least Squares Support Vector Regression. Neural Process Lett 38, 81–96 (2013). https://doi.org/10.1007/s11063-012-9264-7

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  • DOI: https://doi.org/10.1007/s11063-012-9264-7

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