IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v39y2020i1p54-70.html
   My bibliography  Save this article

Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models

Author

Listed:
  • Fredj Jawadi
  • Zied Ftiti
  • Waël Louhichi

Abstract

This study forecasts the volatility of two energy futures markets (oil and gas), using high-frequency data. We, first, disentangle volatility into continuous volatility and jumps. Second, we apply wavelet analysis to study the relationship between volume and the volatility measures for different horizons. Third, we augment the heterogeneous autoregressive (HAR) model by nonlinearly including both jumps and volume. We then propose different empirical extensions of the HAR model. Our study shows that oil and gas volatilities nonlinearly depend on public information (jumps), private information (continuous volatility), and trading volume. Moreover, our threshold augmented HAR model with heterogeneous jumps and continuous volatility outperforms HAR model in forecasting volatility.

Suggested Citation

  • Fredj Jawadi & Zied Ftiti & Waël Louhichi, 2020. "Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 54-70, January.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:1:p:54-70
    DOI: 10.1080/07474938.2019.1690190
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2019.1690190
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2019.1690190?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. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    2. Jawadi, Fredj & Bourghelle, David & Rozin, Philippe & Cheffou, Abdoulkarim Idi & Uddin, Gazi Salah, 2024. "Sentiment and energy price volatility: A nonlinear high frequency analysis," Energy Economics, Elsevier, vol. 133(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:emetrv:v:39:y:2020:i:1:p:54-70. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.