IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v42y2026i3p833-852.html

Jump persistence and temporal aggregation of tail risk

Author

Listed:
  • Zhou, Chunyang
  • Wu, Chongfeng
  • Wan, Xiangwei

Abstract

Major events can have a lasting impact on the financial markets and affect the temporal aggregation of tail risk. We capture the dynamics of jump intensity using a generalized autoregressive conditional heteroskedasticity with autoregressive jump intensity (GARCH-ARJI) model, derive analytical formulas for the first four moments of cumulative returns, and utilize them to calculate VaR based on the Johnson distribution method. Our numerical experiments reveal that skewness decreases sharply while excess kurtosis rises in the short term, particularly when initial jump intensity is high. In the long term, the time diversification effect causes skewness and excess kurtosis to converge slowly to zero. Our out-of-sample backtesting analysis on S&P 500, FTSE 100, and DAX 30 total return indexes shows that it is important to incorporate the time-varying jump intensity when forecasting tail risk.

Suggested Citation

  • Zhou, Chunyang & Wu, Chongfeng & Wan, Xiangwei, 2026. "Jump persistence and temporal aggregation of tail risk," International Journal of Forecasting, Elsevier, vol. 42(3), pages 833-852.
    • Handle: RePEc:eee:intfor:v:42:y:2026:i:3:p:833-852
    DOI: 10.1016/j.ijforecast.2025.11.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207025001190
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2025.11.010?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

    for a different version of it.

    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:eee:intfor:v:42:y:2026:i:3:p:833-852. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.