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Insurance Supervision under Climate Change: A Pioneer Detection Method

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  • Eric Vansteenberhge

Abstract

We present the Pioneer Detection Method, a supervisory tool we developed to enhance resilience in insurance markets facing the challenges posed by climate change. Based on a theoretical model of the insurance industry, we consider a scenario in which independent experts determine premiums according to their individual risk assessments. Due to the segmented nature of the private insurance market, accurately estimating the tail parameter of loss distribution is difficult, especially given the rarity of extreme events. Our method leverages temporal directional change and convergence to integrate expert opinions, giving greater emphasis to those who effectively identify trend shifts after climate stress. A series of simulations reveals that the Pioneer Detection Method outperforms traditional pooling methods within a Bayesian framework. Furthermore, this approach appears to be notably effective in improving welfare in an insurance market with a limited number of private entities.

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  • Eric Vansteenberhge, 2025. "Insurance Supervision under Climate Change: A Pioneer Detection Method," Papers 2511.16760, arXiv.org.
    • Handle: RePEc:arx:papers:2511.16760
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    1. Markus K. Brunnermeier & Alp Simsek & Wei Xiong, 2014. "A Welfare Criterion For Models With Distorted Beliefs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1753-1797.
    2. Hasbrouck, Joel, 1995. "One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
    3. Kousky, Carolyn, 2014. "Informing climate adaptation: A review of the economic costs of natural disasters," Energy Economics, Elsevier, vol. 46(C), pages 576-592.
    4. Moran Cerf & Sandra C. Matz & Malcolm A. MacIver, 2023. "Participating in a climate prediction market increases concern about global warming," Nature Climate Change, Nature, vol. 13(6), pages 523-531, June.
    5. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    6. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    7. Peter Ditlevsen & Susanne Ditlevsen, 2023. "Warning of a forthcoming collapse of the Atlantic meridional overturning circulation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    8. Moran Cerf & Sandra C. Matz & Malcolm A. MacIver, 2023. "Publisher Correction: Participating in a climate prediction market increases concern about global warming," Nature Climate Change, Nature, vol. 13(9), pages 997-997, September.
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