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A STATISTICAL METHODOLOGY FOR ASSESSING THE MAXIMAL STRENGTH OF TAIL DEPENDENCE

Published online by Cambridge University Press:  29 June 2020

Ning Sun ,
Chen Yang Open the ORCID record for Chen Yang [Opens in a new window]  and
Ričardas Zitikis
Ning Sun
Affiliation:
School of Mathematical and Statistical Sciences, Western University, London, ONN6A 5B7, Canada
Chen Yang*
Affiliation:
School of Mathematical and Statistical Sciences, Western University, London, ONN6A 5B7, Canada Economics and Management School, Wuhan University, Wuhan, Hubei430072, People’s Republic of China, E-Mail: [email protected]
Ričardas Zitikis
Affiliation:
School of Mathematical and Statistical Sciences, Western University, London, ONN6A 5B7, Canada Risk and Insurance Studies Centre, York University, Toronto, ONM3J 1P3, Canada
*
E-Mail: [email protected]
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Abstract

Several diagonal-based tail dependence indices have been suggested in the literature to quantify tail dependence. They have well-developed statistical inference theories but tend to underestimate tail dependence. For those problems when assessing the maximal strength of dependence is important (e.g., co-movements of financial instruments), the maximal tail dependence index was introduced, but it has so far lacked empirical estimators and statistical inference results, thus hindering its practical use. In the present paper, we suggest an empirical estimator for the index, explore its statistical properties, and illustrate its performance on simulated data.

Keywords

Copulamaximal tail dependencestatistical estimationKendall’s distribution
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 3 , September 2020 , pp. 799 - 825
Copyright
© 2020 by Astin Bulletin. All rights reserved

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