IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v87y2017i2d10.1007_s11069-017-2784-8.html
   My bibliography  Save this article

Statistical tests for underestimation of loss distributions from NatCat models

Author

Listed:
  • Mathias Raschke

Abstract

In the (re)insurance industry, the risk from natural hazards such as earthquakes or floods is quantified by models for natural catastrophes, also called NatCat models. One important output of NatCat models is the distribution function for the annual maximum of loss occurrence. The opportunities to test this fully specified distribution with a statistical test are limited as only the m-largest annual maxima of loss occurrence are known for a sample of n years in most cases. Here, new test statistics are introduced to validate the NatCat models against underestimation. The basis is the transformation of the largest realisations by a conditional distribution of order statistics. The resulting realisations of a new random variable are independent, and their distribution is known if H 0 is correct. Feasibility test statistics are formulated. The most powerful of these is a linear combination of normally distributed random variables. It performs much better than the conventional Kolmogorov–Smirnov for the conditional distribution of the excess. Different issues and weak points of all test statistics are briefly discussed.

Suggested Citation

  • Mathias Raschke, 2017. "Statistical tests for underestimation of loss distributions from NatCat models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(2), pages 1251-1258, June.
    • Handle: RePEc:spr:nathaz:v:87:y:2017:i:2:d:10.1007_s11069-017-2784-8
    DOI: 10.1007/s11069-017-2784-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-017-2784-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-017-2784-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:nathaz:v:87:y:2017:i:2:d:10.1007_s11069-017-2784-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.