IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-01299692.html
   My bibliography  Save this paper

Wind Storm Risk Management

Author

Listed:
  • Alexandre Mornet

    (LSAF - 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

    (LSAF - 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-01299692v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01299692v1/document
    Download Restriction: no
    ---><---

    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. 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.
    3. Cameron A. MacKenzie, 2014. "Summarizing Risk Using Risk Measures and Risk Indices," Risk Analysis, John Wiley & Sons, vol. 34(12), pages 2143-2162, December.
    4. 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.
    5. 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.
    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    3. Silvia Facchinetti & Paolo Giudici & Silvia Angela Osmetti, 2020. "Cyber risk measurement with ordinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 173-185, March.
    4. Sara Ali Alokley & Mansour Saleh Albarrak, 2020. "Clustering of Extremes in Financial Returns: A Study of Developed and Emerging Markets," JRFM, MDPI, vol. 13(7), pages 1-11, July.
    5. Paola Bortot & Carlo Gaetan, 2016. "Latent Process Modelling of Threshold Exceedances in Hourly Rainfall Series," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 531-547, September.
    6. Stefan Rass & Sandra König & Stefan Schauer, 2017. "Defending Against Advanced Persistent Threats Using Game-Theory," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-43, January.
    7. Stefan Hochrainer-Stigler & JoAnne Linnerooth-Bayer & Junko Mochizuki, 2019. "Flood Proofing Low-Income Houses in India: an Application of Climate-Sensitive Probabilistic Benefit-Cost Analysis," Economics of Disasters and Climate Change, Springer, vol. 3(1), pages 23-38, April.
    8. Marta Ferreira, 2024. "Extremal index: estimation and resampling," Computational Statistics, Springer, vol. 39(5), pages 2703-2720, July.
    9. 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.
    10. Beirlant, J. & Schoutens, W. & Segers, J.J.J., 2004. "Mandelbrot's Extremism," Discussion Paper 2004-125, Tilburg University, Center for Economic Research.
    11. Gloria Buriticá & Philippe Naveau, 2023. "Stable sums to infer high return levels of multivariate rainfall time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.
    12. Fries, Sébastien & Zakoian, Jean-Michel, 2019. "Mixed Causal-Noncausal Ar Processes And The Modelling Of Explosive Bubbles," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1234-1270, December.
    13. A. P. Martins & J. R. Sebastião, 2019. "Methods for estimating the upcrossings index: improvements and comparison," Statistical Papers, Springer, vol. 60(4), pages 1317-1347, August.
    14. Segers, J.J.J., 2006. "Rare Events, Temporal Dependence and the Extremal Index," Other publications TiSEM 04952d0f-2b24-44ad-bf07-f, Tilburg University, School of Economics and Management.
    15. J. Sebastião & A. Martins & H. Ferreira & L. Pereira, 2013. "Estimating the upcrossings index," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 549-579, November.
    16. Zhao, Xin & Scarrott, Carl John & Oxley, Les & Reale, Marco, 2011. "GARCH dependence in extreme value models with Bayesian inference," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1430-1440.
    17. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    18. Andreas Tsanakas & Pietro Millossovich, 2016. "Sensitivity Analysis Using Risk Measures," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 30-48, January.
    19. Amir AghaKouchak & Nasrin Nasrollahi, 2010. "Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1229-1249, April.
    20. Busby, J.S., 2019. "The co-evolution of competition and parasitism in the resource-based view: A risk model of product counterfeiting," European Journal of Operational Research, Elsevier, vol. 276(1), pages 300-313.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-01299692. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.