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A biased-randomized algorithm for optimizing efficiency in parametric earthquake (Re) insurance solutions

  • Identification data

    Identifier: imarina:6684952
    Authors:
    Bayliss CGuidotti REstrada-Moreno AFranco GJuan AA
    Abstract:
    © 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.
  • Others:

    Author, as appears in the article.: Bayliss C; Guidotti R; Estrada-Moreno A; Franco G; Juan AA
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Estrada Moreno, Alejandro
    Keywords: Trigger Risk analysis Parametric insurance Combinatorial optimization Catastrophe bonds Biased randomization Basis risk
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: alejandro.estrada@urv.cat
    Author identifier: 0000-0001-9767-2177
    Record's date: 2023-04-30
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0305054820301507
    Papper original source: Computers & Operations Research. 123 (105033):
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1016/j.cor.2020.105033
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    Computer Science (Miscellaneous),Computer Science, Interdisciplinary Applications,Engineering, Industrial,Management Science and Operations Research,Modeling and Simulation,Operations Research & Management Science
    Trigger
    Risk analysis
    Parametric insurance
    Combinatorial optimization
    Catastrophe bonds
    Biased randomization
    Basis risk
    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
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