IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v22y2006i2p155-167.html
   My bibliography  Save this article

A Bayesian analysis of clusters of extreme losses

Author

Listed:
  • Beatriz Vaz de Melo Mendes

Abstract

Occasional short range dependence in a claim generating process, probably induced by some subordinated process, may result in clusters of extreme losses. Under such circumstances, the iid assumption for the exceedances may not hold anymore. For a given value of the retention level, we overcome this difficulty by applying an empirical rule for cluster definition and aggregating the excesses within clusters. This modelling strategy is compared to the classical random sum of excess losses model based on the iid assumption. The usual discrete probability models and an extreme value distribution from the Pareto family are assumed, respectively, for the counting processes and the severities. Bayesian techniques are then used to obtain a predictive distribution of the annual excess claim amount. Maximum likelihood estimates are also computed and compared. We illustrate using a fire insurance claims data. The Bayesian approach provided more conservative point and interval estimates for the statistical premium. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Beatriz Vaz de Melo Mendes, 2006. "A Bayesian analysis of clusters of extreme losses," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(2), pages 155-167, March.
    • Handle: RePEc:wly:apsmbi:v:22:y:2006:i:2:p:155-167
    DOI: 10.1002/asmb.625
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.625
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.625?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Ausloos, Marcel & Cerqueti, Roy & Bartolacci, Francesca & Castellano, Nicola G., 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 754-765.
    2. Mora Valencia Andrés, 2014. "El uso de la distribución g-h en riesgo operativo," Contaduría y Administración, Accounting and Management, vol. 59(1), pages 123-148, enero-mar.

    More about this item

    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:wly:apsmbi:v:22:y:2006:i:2:p:155-167. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.