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MULTIVARIATE GEOMETRIC TAIL- AND RANGE-VALUE-AT-RISK

Published online by Cambridge University Press:  21 October 2019

Klaus Herrmann Open the ORCID record for Klaus Herrmann [Opens in a new window] ,
Marius Hofert  and
Mélina Mailhot
Klaus Herrmann*
Affiliation:
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, E-Mail: klaus.herrmann@uwaterloo.ca
Marius Hofert
Affiliation:
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, E-Mail: marius.hofert@uwaterloo.ca
Mélina Mailhot
Affiliation:
Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada, E-Mail: melina.mailhot@concordia.ca
*
E-Mail: klaus.herrmann@uwaterloo.ca
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Abstract

A generalization of range-value-at-risk (RVaR) and tail-value-at-risk (TVaR) for d-dimensional distribution functions is introduced. Properties of these new risk measures are studied and illustrated. We provide special cases, applications and a comparison with traditional univariate and multivariate versions of the TVaR and RVaR.

Keywords

Multivariate risk measurestail-value-at-riskrange-value-at-riskgeometric quantilesdependence

Information

Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 1 , January 2020 , pp. 265 - 292
Copyright
© Astin Bulletin 2019 

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