Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/191965
Title: Basis risk management and randomly scaled uncertainty
Author: Claramunt Bielsa, M. Mercè
Lefèvre, Claude
Loisel, Stéphane
Montesinos, Pierre
Keywords: Risc (Assegurances)
Funcions convexes
Incertesa
Variables aleatòries
Risk (Insurance)
Convex functions
Uncertainty
Random variables
Issue Date: 1-Nov-2022
Publisher: Elsevier B.V.
Abstract: This paper proposes a method for quantifying the basis risk present in index-based insurance. It applies when the inherent uncertainty is represented by a randomly scaled variable. This turns out to be a reasonable assumption in a number of practical situations. Several properties of such a variable are first briefly studied. Their order in the s-convex sense is discussed and the associated extreme distributions are obtained to generate the worst situations. In each scenario, the basis risk consequences are then assessed using a penalty function that takes into account the risk tolerances of the protection buyer. Basis risk limits for a fixed budget can also be set. The proposed approach is illustrated by a few simple examples.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.insmatheco.2022.08.005
It is part of: Insurance Mathematics and Economics, 2022, vol. 107, p. 123-139
URI: http://hdl.handle.net/2445/191965
Related resource: https://doi.org/10.1016/j.insmatheco.2022.08.005
ISSN: 0167-6687
Appears in Collections:Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

Files in This Item:
File Description SizeFormat 
727523.pdf343.24 kBAdobe PDFView/Open    Request a copy


Embargat   Document embargat fins el 1-11-2024


This item is licensed under a Creative Commons License Creative Commons