IDEAS home Printed from https://ideas.repec.org/a/aen/journl/ej44-1-zhang.html
   My bibliography  Save this article

Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?

Author

Listed:
  • Yue-Jun Zhang and Han Zhang

Abstract

GARCH-type models have been widely used for forecasting crude oil price volatility, but often ignore the structural changes of time series, which may lead to spurious volatility persistence. Therefore, this paper focuses on the smooth and sharp structural changes in crude oil price volatility, i.e., smooth shift and regime switching, respectively, and investigates which structural change based GARCH models have better performance for forecasting crude oil price volatility. The empirical results indicate that, first, the flexible Fourier form (FFF) GARCH-type models considering smooth shift can accurately model structural changes and yield superior fitting and forecasting performance to traditional GARCH-type models. Second, the Markov regime switching (MRS) GARCH model incorporating regime switching exhibits superior fitting performance compared to the single-regime GARCH-type models, but it does not necessarily beat the counterparts for forecasting. Finally, the FFF-GARCH-type models outperform MRS-GARCH for forecasting crude oil price volatility and portfolio performance.

Suggested Citation

  • Yue-Jun Zhang and Han Zhang, 2023. "Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    • Handle: RePEc:aen:journl:ej44-1-zhang
    as

    Download full text from publisher

    File URL: http://www.iaee.org/en/publications/ejarticle.aspx?id=3933
    Download Restriction: Access to full text is restricted to IAEE members and subscribers.
    ---><---

    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. Hu, Genhua & Jiang, Haifeng, 2023. "Time-varying jumps in China crude oil futures market impacted by COVID-19 pandemic," Resources Policy, Elsevier, vol. 82(C).
    2. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    3. Xiao, Jihong & Wang, Yudong & Wen, Danyan, 2023. "The predictive effect of risk aversion on oil returns under different market conditions," Energy Economics, Elsevier, vol. 126(C).
    4. Ting Yao & Yue‐Jun Zhang, 2024. "The impact of air pollution on crude oil futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 1055-1068, June.
    5. Feng, Lingbing & Rao, Haicheng & Lucey, Brian & Zhu, Yiying, 2024. "Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1595-1615.
    6. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).

    More about this item

    JEL classification:

    • F0 - International Economics - - General

    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:aen:journl:ej44-1-zhang. 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: David Williams (email available below). General contact details of provider: https://edirc.repec.org/data/iaeeeea.html .

    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.