IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-89824-7_15.html
   My bibliography  Save this book chapter

Classification Ratemaking via Quantile Regression and a Comparison with Generalized Linear Models

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Fabio Baione

    (Catholic University, Department of Mathematical Sciences, Mathematical Finance and Econometrics)

  • Davide Biancalana

    (Sapienza University of Rome, Department of Statistical Sciences)

  • Paolo De Angelis

    (Sapienza University of Rome, Department of Methods and Models for Economics Territory and Finance)

  • Ivan Granito

    (Sapienza University of Rome, Department of Statistical Sciences)

Abstract

In non-life insurance, it is important to develop a loaded premium for individual risks, as the sum of a pure premium (expected value of loss) and a safety loading or risk margin. In actuarial practice, this process is known as classification ratemaking and is performed usually via Generalized Linear Model. The latter permits an estimate of individual pure premium and safety loading both; however, the goodness of the estimates are strongly related to the compliance of the model assumption with the empirical distribution. In order to investigate the individual pure premium, we introduce an alternative pricing model based on Quantile Regression, to perform a working classification ratemaking with weaker assumptions and, then, more performing for risk margin evaluation.

Suggested Citation

  • Fabio Baione & Davide Biancalana & Paolo De Angelis & Ivan Granito, 2018. "Classification Ratemaking via Quantile Regression and a Comparison with Generalized Linear Models," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 87-91, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_15
    DOI: 10.1007/978-3-319-89824-7_15
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-319-89824-7_15. 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.