Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement

Yanxi Hou and Xing Wang

Journal of the American Statistical Association, 2021, vol. 116, issue 535, 1428-1443

Abstract: Abstract–Tail risk analysis focuses on the problem of risk measurement on the tail regions of financial variables. As one crucial task in tail risk analysis for risk management, the measurement of tail risk variability is less addressed in the literature. Neither the theoretical results nor inference methods are fully developed, which results in the difficulty of modeling implementation. Practitioners are then short of measurement methods to understand and evaluate tail risks, even when they have large amounts of valuable data in hand. In this article, we consider the measurement of tail variability under the tail scenarios of a systemic variable by extending the Gini’s methodology. As we are very interested in the limit of the proposed measures as the risk level approaches to the extreme status, we showed, by using extreme value techniques, how the tail dependence structure and marginal risk severity have influences on the limit of the proposed tail variability measures. We construct a nonparametric estimator, and its asymptotic behavior is explored. Furthermore, to provide practitioners with more measures for tail risk, we construct three coefficients/measures for tail risks from different views toward tail risks and illustrate them in a real data analysis. Supplementary materials for this article are available online.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2020.1730855 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:116:y:2021:i:535:p:1428-1443

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2020.1730855

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().