A decomposition of general premium principles into risk and deviation
Max Nendel,
Frank Riedel and
Maren Diane Schmeck
Insurance: Mathematics and Economics, 2021, vol. 100, issue C, 193-209
Abstract:
We provide an axiomatic approach to general premium principles in a probability-free setting that allows for Knightian uncertainty. Every premium principle is the sum of a risk measure, as a generalization of the expected value, and a deviation measure, as a generalization of the variance. One can uniquely identify a maximal risk measure and a minimal deviation measure in such decompositions. We show how previous axiomatizations of premium principles can be embedded into our more general framework. We discuss dual representations of convex premium principles, and study the consistency of premium principles with a financial market in which insurance contracts are traded.
Keywords: Principle of premium calculation; Risk measure; Deviation measure; Convex duality; Superhedging (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167668721000895
Full text for ScienceDirect subscribers only
Related works:
Working Paper: A decomposition of general premium principles into risk and deviation (2020) 
Working Paper: A Decompostion of General Premium Principles into Risk and Deviation (2020) 
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:eee:insuma:v:100:y:2021:i:c:p:193-209
DOI: 10.1016/j.insmatheco.2021.05.006
Access Statistics for this article
Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu
More articles in Insurance: Mathematics and Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().