Autor segons l'article: Bayliss C; Guidotti R; Estrada-Moreno A; Franco G; Juan AA
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Estrada Moreno, Alejandro
Paraules clau: Trigger Risk analysis Parametric insurance Combinatorial optimization Catastrophe bonds Biased randomization Basis risk
Resum: © 2020 Elsevier Ltd Natural catastrophes with their widespread damage can overwhelm the financial systems of large communities. Catastrophe insurance is a well-understood financial risk transfer mechanism, aiming to provide resilience in the face of adversity. However, catastrophe insurance has generally a low penetration, mainly due to its high cost or to distrust of the product in providing a fast financial recovery. Parametric insurance is a form of derivative insurance that pays quickly and transparently based on a few measurable features of the event, offering a promising avenue to increase catastrophe insurance coverage. In the context of seismic risk, parametric policies may use location and magnitude of an earthquake to determine whether a payment should be made. In this paper we follow a design typology referred to as ‘cat-in-a-box’, where magnitude thresholds are defined over a set of cuboids that partition Earth's crust. The main challenge in the design of these tools consists in finding the optimal magnitude thresholds for a large set of cubes that maximize efficiency for the insured, subjected to a budgetary constraint. Additional geometric constraints aim to reduce the volatility of payments under uncertainty. The parametric design problem is a combinatorial problem, which is NP-hard and large scale. In this paper we propose a fast heuristic and a biased-randomized algorithm to solve large-sized problems in reasonably low computing times. Experimental results illustrate the computational limits and solution quality associated with the proposed approaches.
Àrees temàtiques: Operations research & management science Modeling and simulation Matemática / probabilidade e estatística Management science and operations research Interdisciplinar General computer science Engineering, industrial Engenharias iv Engenharias iii Engenharias ii Engenharias i Economia Computer science, interdisciplinary applications Computer science (miscellaneous) Computer science (all) Ciencias sociales Ciência da computação Biotecnología Arquitetura e urbanismo Administração pública e de empresas, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: alejandro.estrada@urv.cat
Identificador de l'autor: 0000-0001-9767-2177
Data d'alta del registre: 2023-04-30
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0305054820301507
Referència a l'article segons font original: Computers & Operations Research. 123 (105033):
Referència de l'ítem segons les normes APA: Bayliss C; Guidotti R; Estrada-Moreno A; Franco G; Juan AA (2020). A biased-randomized algorithm for optimizing efficiency in parametric earthquake (Re) insurance solutions. Computers & Operations Research, 123(105033), -. DOI: 10.1016/j.cor.2020.105033
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.1016/j.cor.2020.105033
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications