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A geometric investigation into the tail dependence of vine copulas

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A geometric investigation into the tail dependence of vine copulas. / Simpson, Emma; Wadsworth, Jennifer; Tawn, Jonathan.
In: Journal of Multivariate Analysis, Vol. 184, 104736, 01.07.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Simpson, E, Wadsworth, J & Tawn, J 2021, 'A geometric investigation into the tail dependence of vine copulas', Journal of Multivariate Analysis, vol. 184, 104736. https://doi.org/10.1016/j.jmva.2021.104736

APA

Simpson, E., Wadsworth, J., & Tawn, J. (2021). A geometric investigation into the tail dependence of vine copulas. Journal of Multivariate Analysis, 184, Article 104736. https://doi.org/10.1016/j.jmva.2021.104736

Vancouver

Simpson E, Wadsworth J, Tawn J. A geometric investigation into the tail dependence of vine copulas. Journal of Multivariate Analysis. 2021 Jul 1;184:104736. Epub 2021 Feb 15. doi: 10.1016/j.jmva.2021.104736

Author

Simpson, Emma ; Wadsworth, Jennifer ; Tawn, Jonathan. / A geometric investigation into the tail dependence of vine copulas. In: Journal of Multivariate Analysis. 2021 ; Vol. 184.

Bibtex

@article{b85b970b43b44050aa3e1118ebb96058,
title = "A geometric investigation into the tail dependence of vine copulas",
abstract = "Vine copulas are a type of multivariate dependence model, composed of a collection of bivariate copulas that are combined according to a specific underlying graphical structure. Their flexibility and practicality in moderate and high dimensions have contributed to the popularity of vine copulas, but relatively little attention has been paid to their extremal properties. To address this issue, we present results on the tail dependence properties of some of the most widely studied vine copula classes. We focus our study on the coefficient of tail dependence and the asymptotic shape of the sample cloud, which we calculate using the geometric approach of Nolde (2014). We offer new insights by presenting results for trivariate vine copulas constructed from asymptotically dependent and asymptotically independent bivariate copulas, focusing on bivariate extreme value and inverted extreme value copulas, with additional detail provided for logistic and inverted logistic examples. We also present new theory for a class of higher dimensional vine copulas, constructed from bivariate inverted extreme value copulas.",
author = "Emma Simpson and Jennifer Wadsworth and Jonathan Tawn",
year = "2021",
month = jul,
day = "1",
doi = "10.1016/j.jmva.2021.104736",
language = "English",
volume = "184",
journal = "Journal of Multivariate Analysis",
issn = "0047-259X",
publisher = "Academic Press Inc.",

}

RIS

TY - JOUR

T1 - A geometric investigation into the tail dependence of vine copulas

AU - Simpson, Emma

AU - Wadsworth, Jennifer

AU - Tawn, Jonathan

PY - 2021/7/1

Y1 - 2021/7/1

N2 - Vine copulas are a type of multivariate dependence model, composed of a collection of bivariate copulas that are combined according to a specific underlying graphical structure. Their flexibility and practicality in moderate and high dimensions have contributed to the popularity of vine copulas, but relatively little attention has been paid to their extremal properties. To address this issue, we present results on the tail dependence properties of some of the most widely studied vine copula classes. We focus our study on the coefficient of tail dependence and the asymptotic shape of the sample cloud, which we calculate using the geometric approach of Nolde (2014). We offer new insights by presenting results for trivariate vine copulas constructed from asymptotically dependent and asymptotically independent bivariate copulas, focusing on bivariate extreme value and inverted extreme value copulas, with additional detail provided for logistic and inverted logistic examples. We also present new theory for a class of higher dimensional vine copulas, constructed from bivariate inverted extreme value copulas.

AB - Vine copulas are a type of multivariate dependence model, composed of a collection of bivariate copulas that are combined according to a specific underlying graphical structure. Their flexibility and practicality in moderate and high dimensions have contributed to the popularity of vine copulas, but relatively little attention has been paid to their extremal properties. To address this issue, we present results on the tail dependence properties of some of the most widely studied vine copula classes. We focus our study on the coefficient of tail dependence and the asymptotic shape of the sample cloud, which we calculate using the geometric approach of Nolde (2014). We offer new insights by presenting results for trivariate vine copulas constructed from asymptotically dependent and asymptotically independent bivariate copulas, focusing on bivariate extreme value and inverted extreme value copulas, with additional detail provided for logistic and inverted logistic examples. We also present new theory for a class of higher dimensional vine copulas, constructed from bivariate inverted extreme value copulas.

U2 - 10.1016/j.jmva.2021.104736

DO - 10.1016/j.jmva.2021.104736

M3 - Journal article

VL - 184

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

SN - 0047-259X

M1 - 104736

ER -