Nonparametric estimation of simplified vine copula models: comparison of methods

• Autorzy: Thomas Nagler , Christian Schellhase , Claudia Czado
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

B-spline; Bernstein; copula; kernel; nonparametric; simulation; vine

• Wydawca: De Gruyter Open
• Czasopismo: Dependence Modeling
• Rocznik: 2017
• Tom: 5
• Numer: 1
• Strony: 99-120

Daty

wydano

2017-01-26

otrzymano

2016-12-27

zaakceptowano

2017-05-16

online

2017-06-27

Twórcy

autor

Thomas Nagler

• ,

autor

Christian Schellhase

• ,

autor

Claudia Czado

• ,

Identyfikatory

DOI

10.1515/demo-2017-0007