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Wind Storm Risk Management

Author

Listed:
  • Alexandre Mornet

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Thomas Opitz

    (BioSP - Biostatistique et Processus Spatiaux - INRA - Institut National de la Recherche Agronomique)

  • Michel Luzi

    (Allianz)

  • Stéphane Loisel

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

Models and forecasts of damage from wind storms are a major issue for insurance companies. In this article, we focus on the calculation sensitivity of return periods for extreme events. Numerous elements come into play, such as data quality (location of insured buildings, weather report homogeneity), missing updates (history of insurance portfolios, change of ground roughness, climate change), the evolution of the model after an unprecedented event such as Lothar in Europe and temporal aggregation (events defined through blocks of 2 or 3 days or blocks of one week). Another important aspect concerns storm trajectories, which could change due to global warming or sweep larger areas. We here partition the French territory into 6 storm zones depending on extreme wind correlation to test several scenarios. We use a storm index defined in \cite{Ma} to show the difficulties met to obtain reliable results on extreme events.

Suggested Citation

  • Alexandre Mornet & Thomas Opitz & Michel Luzi & Stéphane Loisel, 2016. "Wind Storm Risk Management," Working Papers hal-01299692, HAL.
    • Handle: RePEc:hal:wpaper:hal-01299692
    Note: View the original document on HAL open archive server: https://hal.science/hal-01299692
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    References listed on IDEAS

    as
    1. Alexandre Mornet & Thomas Opitz & Michel Luzi & Stéphane Loisel, 2015. "Index for predicting insurance claims from wind storms with an application in France," Post-Print hal-01081758, HAL.
    2. Lelys Bravo Guenni & Susan J. Simmons & Stefan Hochrainer‐Stigler & Georg Pflug, 2012. "Risk management against extremes in a changing environment: a risk‐layer approach using copulas," Environmetrics, John Wiley & Sons, Ltd., vol. 23(8), pages 663-672, December.
    3. Alexandre Mornet & Thomas Opitz & Michel Luzi & Stéphane Loisel, 2015. "Index for Predicting Insurance Claims from Wind Storms with an Application in France," Risk Analysis, John Wiley & Sons, vol. 35(11), pages 2029-2056, November.
    4. Cameron A. MacKenzie, 2014. "Summarizing Risk Using Risk Measures and Risk Indices," Risk Analysis, John Wiley & Sons, vol. 34(12), pages 2143-2162, December.
    5. Christopher A. T. Ferro & Johan Segers, 2003. "Inference for clusters of extreme values," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 545-556, May.
    6. Robert L. Winkler, 2015. "The Importance of Communicating Uncertainties in Forecasts: Overestimating the Risks from Winter Storm Juno," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 349-353, March.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Stéphane Loisel, 2014. "Reevaluation of the capital charge in insurance after a large shock: empirical and theoretical views," Post-Print hal-02013669, HAL.

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