IDEAS home Printed from https://ideas.repec.org/a/hin/jnljps/596839.html
   My bibliography  Save this article

Estimating the Conditional Tail Expectation in the Case of Heavy-Tailed Losses

Author

Listed:
  • Abdelhakim Necir
  • Abdelaziz Rassoul
  • Ričardas Zitikis

Abstract

The conditional tail expectation (CTE) is an important actuarial risk measure and a useful tool in financial risk assessment. Under the classical assumption that the second moment of the loss variable is finite, the asymptotic normality of the nonparametric CTE estimator has already been established in the literature. The noted result, however, is not applicable when the loss variable follows any distribution with infinite second moment, which is a frequent situation in practice. With a help of extreme-value methodology, in this paper, we offer a solution to the problem by suggesting a new CTE estimator, which is applicable when losses have finite means but infinite variances.

Suggested Citation

  • Abdelhakim Necir & Abdelaziz Rassoul & Ričardas Zitikis, 2010. "Estimating the Conditional Tail Expectation in the Case of Heavy-Tailed Losses," Journal of Probability and Statistics, Hindawi, vol. 2010, pages 1-17, April.
  • Handle: RePEc:hin:jnljps:596839
    DOI: 10.1155/2010/596839
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JPS/2010/596839.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/JPS/2010/596839.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2010/596839?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
    ---><---

    Citations

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


    Cited by:

    1. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Weighted allocations, their concomitant-based estimators, and asymptotics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 811-835, August.
    2. Haoyu Chen & Tiantian Mao & Fan Yang, 2024. "Estimation of the Adjusted Standard-deviatile for Extreme Risks," Papers 2411.07203, arXiv.org.
    3. Yunran Wei & Ricardas Zitikis, 2022. "Assessing the difference between integrated quantiles and integrated cumulative distribution functions," Papers 2210.16880, arXiv.org, revised Apr 2023.
    4. Wei, Yunran & Zitikis, Ričardas, 2023. "Assessing the difference between integrated quantiles and integrated cumulative distribution functions," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 163-172.

    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:hin:jnljps:596839. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.