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Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

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Listed:
  • T. Ermolieva
  • T. Filatova
  • Y. Ermoliev
  • M. Obersteiner
  • K. M. de Bruijn
  • A. Jeuken

Abstract

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.

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  • T. Ermolieva & T. Filatova & Y. Ermoliev & M. Obersteiner & K. M. de Bruijn & A. Jeuken, 2017. "Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 82-98, January.
  • Handle: RePEc:wly:riskan:v:37:y:2017:i:1:p:82-98
    DOI: 10.1111/risa.12589
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    References listed on IDEAS

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    1. W. Botzen & J. Bergh & L. Bouwer, 2010. "Climate change and increased risk for the insurance sector: a global perspective and an assessment for the Netherlands," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(3), pages 577-598, March.
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    Cited by:

    1. Peter John Robinson & W. J. Wouter Botzen, 2022. "Setting descriptive norm nudges to promote demand for insurance against increasing climate change risk," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(1), pages 27-49, January.
    2. Arnaud Mignan, 2022. "Categorizing and Harmonizing Natural, Technological, and Socio-Economic Perils Following the Catastrophe Modeling Paradigm," IJERPH, MDPI, vol. 19(19), pages 1-32, October.
    3. Marco Criado & Antonio Martínez-Graña & Javier Sánchez San Román & Fernando Santos-Francés, 2018. "Flood Risk Evaluation in Urban Spaces: The Study Case of Tormes River (Salamanca, Spain)," IJERPH, MDPI, vol. 16(1), pages 1-19, December.
    4. Peter John Robinson & W. J. Wouter Botzen & Fujin Zhou, 2021. "An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand," Journal of Risk and Uncertainty, Springer, vol. 63(3), pages 275-318, December.
    5. Yi, Changsheng & Chen, Zhaoming & Chen, Hongchen, 2023. "Opportunity knocks but just once: Impact of infrastructure investment decision on climate adaptation to flood events," Omega, Elsevier, vol. 121(C).
    6. Peter John Robinson & W. J. Wouter Botzen, 2019. "Determinants of Probability Neglect and Risk Attitudes for Disaster Risk: An Online Experimental Study of Flood Insurance Demand among Homeowners," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2514-2527, November.

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