IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i17p4096-4118.html
   My bibliography  Save this article

Bayesian estimation of ruin probability based on NHPP claim arrivals and Inverse-Gaussian distributed claim aggregates

Author

Listed:
  • M. S. Aminzadeh
  • Min Deng

Abstract

The purpose of this article is to estimate the ruin probability at a future time Tτ past a truncated time τ before which ruin has not occurred. It is assumed that claim arrivals are from a non-homogenous Poisson process (NHPP). The distribution of claim amount X is assumed to be heavy-tailed such as Inverse-Gaussian (IG). Gamma priors are used to find Bayes estimates of IG parameters as well as the parameter of the intensity function using an MCMC algorithm. Based on observed arrival times t1,…,tn, and claim amounts x1,…,xn before the truncated time τ, all parameters associated with the aggregate risk process and the NHPP are estimated to compute the ruin probability. Simulation results are presented to assess the accuracy of Maximum likelihood and Bayes estimates of the ruin probability.

Suggested Citation

  • M. S. Aminzadeh & Min Deng, 2021. "Bayesian estimation of ruin probability based on NHPP claim arrivals and Inverse-Gaussian distributed claim aggregates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(17), pages 4096-4118, August.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:17:p:4096-4118
    DOI: 10.1080/03610926.2019.1710763
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1710763
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1710763?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.

    Citations

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


    Cited by:

    1. Syuhada, Khreshna & Tjahjono, Venansius & Hakim, Arief, 2024. "Compound Poisson–Lindley process with Sarmanov dependence structure and its application for premium-based spectral risk forecasting," Applied Mathematics and Computation, Elsevier, vol. 467(C).

    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:taf:lstaxx:v:50:y:2021:i:17:p:4096-4118. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.