Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach

Marco Bee, Julien Hambuckers and L. Trapin

Quantitative Finance, 2019, vol. 19, issue 8, 1255-1266

Abstract: The g-and-h distribution is able to handle well the complex behavior of loss data and applied to operational losses suggests that indirect inference estimators of VaR outperform quantile-based estimators

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2019.1580762 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach (2018)
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:quantf:v:19:y:2019:i:8:p:1255-1266

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2019.1580762

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

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