The use of flexible quantile-based measures in risk assessment

Jaume Belles-Sampera (), Montserrat Guillen and Miguel Santolino ()
Additional contact information
Jaume Belles-Sampera: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona
Miguel Santolino: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona

No 2014-09, Working Papers from Universitat de Barcelona, UB Riskcenter

Abstract: A new family of distortion risk measures -GlueVaR- is proposed in Belles- Sampera et al. (2014) to procure a risk assessment lying between those provided by common quantile-based risk measures. GlueVaR risk measures may be expressed as a combination of these standard risk measures. We show here that this relationship may be used to obtain approximations of GlueVaR measures for general skewed distribution functions using the Cornish-Fisher expansion. A subfamily of GlueVaR measures satis es the tail-subadditivity property. An example of risk measurement based on real insurance claim data is presented, where implications of tail-subadditivity in the aggregation of risks are illustrated.

Keywords: quantiles; subadditivity; tails; risk management; Value-at-Risk (search for similar items in EconPapers)
Pages: 15 pages
Date: 2015-02
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.ub.edu/rfa/research/WP/UBriskcenterWP201409.pdf First version, 2014 (application/pdf)

Related works:
Working Paper: The use of flexible quantile-based measures in risk assessment (2013)
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:bak:wpaper:201409

Access Statistics for this paper

More papers in Working Papers from Universitat de Barcelona, UB Riskcenter Contact information at EDIRC.
Bibliographic data for series maintained by Montserrat Guillen ().