IDEAS home Printed from https://ideas.repec.org/a/cup/astinb/v40y2010i01p399-414_00.html
   My bibliography  Save this article

Numerical Evaluation of Continuous Time Ruin Probabilities for a Portfolio with Credibility Updated Premiums

Author

Listed:
  • Afonso, Lourdes B.
  • Reis, Alfredo D. Egídio dos
  • Waters, Howard R.

Abstract

The probability of ruin in continuous and finite time is numerically evaluated in a classical risk process where the premium can be updated according to credibility models and therefore change from year to year. A major consideration in the development of this approach is that it should be easily applicable to large portfolios. Our method uses as a first tool the model developed by Afonso et al. (2009), which is quite flexible and allows premiums to change annually. We extend that model by introducing a credibility approach to experience rating. We consider a portfolio of risks which satisfy the assumptions of the Bühlmann (1967, 1969) or Bühlmann and Straub (1970) credibility models. We compute finite time ruin probabilities for different scenarios and compare with those when a fixed premium is considered.

Suggested Citation

  • Afonso, Lourdes B. & Reis, Alfredo D. Egídio dos & Waters, Howard R., 2010. "Numerical Evaluation of Continuous Time Ruin Probabilities for a Portfolio with Credibility Updated Premiums," ASTIN Bulletin, Cambridge University Press, vol. 40(1), pages 399-414, May.
    • Handle: RePEc:cup:astinb:v:40:y:2010:i:01:p:399-414_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0515036100000532/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Gauchon, Romain & Loisel, Stéphane & Rullière, Jean-Louis & Trufin, Julien, 2020. "Optimal prevention strategies in the classical risk model," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 202-208.
    2. Romain Gauchon & Stéphane Loisel & Jean-Louis Rullière & Julien Trufin, 2019. "Optimal prevention strategies in the classical risk model," Working Papers hal-02314899, HAL.
    3. Osatakul, Dhiti & Li, Shuanming & Wu, Xueyuan, 2023. "Discrete-time risk models with surplus-dependent premium corrections," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    4. Landriault, David & Lemieux, Christiane & Willmot, Gordon E., 2012. "An adaptive premium policy with a Bayesian motivation in the classical risk model," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 370-378.
    5. Li, Shu & Landriault, David & Lemieux, Christiane, 2015. "A risk model with varying premiums: Its risk management implications," Insurance: Mathematics and Economics, Elsevier, vol. 60(C), pages 38-46.

    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:cup:astinb:v:40:y:2010:i:01:p:399-414_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/asb .

    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.