The Power Principle and Tail-Fatness Uncertainty
Roger Gay ()
No 1/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
Abstract:
When insurance claims are governed by fat-tailed distributions, gross uncertainty about the value of the tail-fatness index is virtually inescapable. In this paper a new premium principle (the power principle) analogous to the exponential principle for thin-tailed claims, is discussed. Pareto premiums determined under the principle have a transparent ratio structure, cater convincingly for uncertainty in the tail-fatness index, and are applicable in passage to the extremal limit, to all fat-tailed distributions in the domain of attraction of the (Frechet) extreme-value distribution. Cover can be provided for part claims if existence of the claims mean is in doubt. Stop-loss premiums are also discussed. Mathematical requirements are very modest.
Keywords: Exponential principle; power principle; constant risk aversion; ratio premium; stop-loss insurance (search for similar items in EconPapers)
JEL-codes: G22 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2004-02
New Economics Papers: this item is included in nep-rmg
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2004/wp1-04.pdf (application/pdf)
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:msh:ebswps:2004-1
Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics
Access Statistics for this paper
More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().