IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v55y2023i22p2551-2566.html
   My bibliography  Save this article

Forecasting stock market realized volatility: the role of global terrorist attacks

Author

Listed:
  • Danyan Wen
  • Mengxi He
  • Yudong Wang
  • Yaojie Zhang

Abstract

In this study, we provide the predictive linkage between global terrorist attacks and stock market volatility. We propose the predictive model by extending the prevailing heterogeneous autoregressive model for realized volatility (HAR-RV) with global terrorism and denote it as HAR-RV-GT. According to the Diebold – Mariano test and the model confidence set, we consistently find the superior forecasting performance delivered by the HAR-RV-GT model. For comprehensive empirical results, we extend the key finding to various settings including the consideration of popular jump and leverage effects, the use of more types of forecasting models, and the inclusion of long-horizon global terrorist attacks as well as the domestic terrorist attacks in the US. Additionally, the substantial economic gains based on a mean-variance investor confirm the valuable forecasting role of terrorist attacks. Our results are robust to a wide range of checks.

Suggested Citation

  • Danyan Wen & Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "Forecasting stock market realized volatility: the role of global terrorist attacks," Applied Economics, Taylor & Francis Journals, vol. 55(22), pages 2551-2566, May.
    • Handle: RePEc:taf:applec:v:55:y:2023:i:22:p:2551-2566
    DOI: 10.1080/00036846.2022.2103503
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2022.2103503
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2022.2103503?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.

    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:applec:v:55:y:2023:i:22:p:2551-2566. 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/RAEC20 .

    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.