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Nonparametric estimation of simplified vine copula models: comparison of methods

Nonparametric estimation of simplified vine copula models: comparison of methods AbstractIn 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

Nonparametric estimation of simplified vine copula models: comparison of methods

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Publisher
de Gruyter
Copyright
© 2017
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2017-0007
Publisher site
See Article on Publisher Site

Abstract

AbstractIn 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.

Journal

Dependence Modelingde Gruyter

Published: Jan 26, 2017

References