The use of the tail dependence function for high quantile risk measure analysis: an application to portfolio optimization

Yuri Salazar Flores, Adán Díaz Hernández, Luis Alberto Quezada-Téllez and Oralia Nolasco Jáuregui

Applied Economics, 2023, vol. 55, issue 37, 4289-4303

Abstract: Adequate risk modelling in a financial portfolio has become the central part of its analysis. To this end, risk measures have proven to be very effective. However, the efficiency of these measures lies in the accurate modelling of both the individual behaviour as well as the dependence between the assets. In particular, tail dependence has become crucial in analysing Value at Risk (VaR) and the Expected Shortfall (ES) in high quantiles. This study introduces a new methodology to estimate high quantile risk measures based on the Tail Dependence Function. With this function, we can estimate asset dependence by focusing on replicating the extreme behaviour. In an empirical study, we estimate the VaR and ES of a portfolio of stock indices during the current pandemic considering our approach along with the most traditional GARCH-Copula and historical approaches as benchmark estimators. Our approach yields superior estimators with respect to the benchmark estimators in high quantiles.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2022.2128183 (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:applec:v:55:y:2023:i:37:p:4289-4303

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2022.2128183

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().