3 résultats pour « NatCat »

EIOPA Urges Clearer NatCat Insurance Disclosures

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This study emphasizes the need for clearer, consumer-friendly disclosures in home insurance policies regarding natural catastrophe coverage. Despite some insurers providing accurate information, vague language and inconsistent definitions in Insurance Product Information Documents (IPIDs) often confuse consumers about coverage for events like floods or fires. With only a quarter of natural disaster losses insured in Europe, unclear disclosures contribute to a significant protection gap, leaving households vulnerable. EIOPA suggests improved IPID design, including detailed peril taxonomies and clear exclusion terms, to help consumers make informed decisions.

FERMA publishes Position Paper on EIOPA and ECB (re)insurance scheme proposal

FERMA supports the EIOPA and ECB's proposal for a European public-private reinsurance scheme to address the natural catastrophe protection gap. While backing the risk-based premium model and the potential for price stability, FERMA emphasizes the need for reliable and consistent data collection across nations. They also highlight the importance of a sufficiently large EU pool to manage premium pricing, a clear regulatory framework avoiding unnecessary burdens, and mechanisms to encourage long-term private sector engagement beyond annual renewals. FERMA advocates for continuous consultation and leveraging the scheme to incentivize risk prevention.

Optimal dividends for a NatCat insurer in the presence of a climate tipping point

This paper extends prior work to model an insurance company facing a future "tipping point" where catastrophe risks increase. Using viscosity solutions of a Hamilton-Jacobi-Bellman equation, the authors solve an optimal control problem to find the best dividend strategy. They show that, under fair premium adjustments and full observability, increased catastrophe risk may benefit shareholders. Numerical examples support these findings, and future research may explore relaxing model assumptions.