Nonparametric estimation of simplified vine copula models: comparison of methods

Nagler Thomas (), Schellhase Christian () and Czado Claudia ()
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Nagler Thomas: Department of Mathematics, Technische Universität München, Boltzmanstraße 3, 85748 Garching, München, Germany
Schellhase Christian: Centre for Statistics, Bielefeld University, Department of Business Administration and Economics, Bielefeld, Germany
Czado Claudia: Department of Mathematics, Technische Universität München, Boltzmanstraße 3, 85748 Garching, München, Germany

Dependence Modeling, 2017, vol. 5, issue 1, 99-120

Abstract: In the last decade, simplified vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densities and bivariate copula densities. Besides parametric models, several approaches to nonparametric estimation of vine copulas have been proposed. In this article, we extend these approaches and compare them in an extensive simulation study and a real data application. We identify several factors driving the relative performance of the estimators. The most important one is the strength of dependence. No method was found to be uniformly better than all others. Overall, the kernel estimators performed best, but do worse than penalized B-spline estimators when there is weak dependence and no tail dependence.

Keywords: B-spline; Bernstein; copula; kernel; nonparametric; simulation; vine (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:5:y:2017:i:1:p:99-120:n:7

DOI: 10.1515/demo-2017-0007

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