IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp1534.html
   My bibliography  Save this paper

Stochastic Claims Reserving Manual: Advances in Dynamic Modeling

Author

Listed:
  • Mario V. Wuthrich

    (ETH Zurich and Swiss Finance Institute)

  • Michael Merz

    (University of Hamburg)

Abstract

These notes are strongly motivated by practitioners who have been seeking for advise in stochastic claims reserving modeling under Solvency 2 and under the Swiss Solvency Test. There have been tremendous developments since the publication of our first book Stochastic Claims Reserving Methods in Insurance in 2008. Particularly the new solvency guidelines have added a dynamic component to claims reserving which has not been present before. This new viewpoint has motivated numerous new developments, for instance, the claims development result and the risk margin were introduced. The present text considers these new aspects, not treated in our previous book, and it should be viewed as completion to our first book.

Suggested Citation

  • Mario V. Wuthrich & Michael Merz, 2015. "Stochastic Claims Reserving Manual: Advances in Dynamic Modeling," Swiss Finance Institute Research Paper Series 15-34, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp1534
    as

    Download full text from publisher

    File URL: http://ssrn.com/abstract=2649057
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Alessandro Ricotta & Edoardo Luini, 2019. "Bayesian Estimation of Structure Variables in the Collective Risk Model for Reserve Risk," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(2), pages 1-2.
    2. Michael Merz & Mario V. Wüthrich, 2015. "Modified Munich Chain-Ladder Method," Risks, MDPI, vol. 3(4), pages 1-23, December.
    3. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    4. Crevecoeur, Jonas & Antonio, Katrien & Verbelen, Roel, 2019. "Modeling the number of hidden events subject to observation delay," European Journal of Operational Research, Elsevier, vol. 277(3), pages 930-944.
    5. Badescu, Andrei L. & Lin, X. Sheldon & Tang, Dameng, 2016. "A marked Cox model for the number of IBNR claims: Theory," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 29-37.
    6. Richard J. Verrall & Mario V. Wüthrich, 2016. "Understanding Reporting Delay in General Insurance," Risks, MDPI, vol. 4(3), pages 1-36, July.
    7. Mathias Lindholm & Ronald Richman & Andreas Tsanakas & Mario V. Wuthrich, 2022. "A Discussion of Discrimination and Fairness in Insurance Pricing," Papers 2209.00858, arXiv.org.
    8. Peters, Gareth W. & Targino, Rodrigo S. & Wüthrich, Mario V., 2017. "Full Bayesian analysis of claims reserving uncertainty," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 41-53.
    9. Gao, Guangyuan & Meng, Shengwang & Shi, Yanlin, 2021. "Dispersion modelling of outstanding claims with double Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 572-586.
    10. England, P.D. & Verrall, R.J. & Wüthrich, M.V., 2019. "On the lifetime and one-year views of reserve risk, with application to IFRS 17 and Solvency II risk margins," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 74-88.
    11. Afaf Antar Zohry & Mostafa Abdelghany Ahmed, 2021. "The Prediction Error of the Chain Ladder Method (With Application to Real Data)," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(12), pages 1-14, December.
    12. Stefano Cavastracci Strascia & Agostino Tripodi, 2018. "Overdispersed-Poisson Model in Claims Reserving: Closed Tool for One-Year Volatility in GLM Framework," Risks, MDPI, vol. 6(4), pages 1-24, December.

    More about this item

    Keywords

    Claims reserving; non-life insurance run-off; chain-ladder method; Bornhuetter-Ferguson method; claims modeling; claims development result; risk margin; run-off uncertainty; conditional mean square error of prediciton;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    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:chf:rpseri:rp1534. 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: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.html .

    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.