IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v21y2021i5p853-863.html
   My bibliography  Save this article

Jumps and oil futures volatility forecasting: a new insight

Author

Listed:
  • Feng Ma
  • Chao Liang
  • Qing Zeng
  • Haibo Li

Abstract

This study designs the Markov-switching (MS) mixed data sampling (MIDAS) models and then explores the effects of intraday and interday jumps on oil futures price realized volatility. The in-sample estimates show that the current realized volatility and jumps have significantly positive impacts on oil futures price volatility using MIDAS models. However, the effects of the jumps are mixed when using MS-MIDAS models. Out-of-sample evaluations show that our proposed model, which includes continuous volatility and jumps based on the Time-of-Day volatility pattern (TOD) test combined with the Markov-switching MIDAS model (MS-MIDAS-TOD), exhibits a higher predictive ability for forecasting one-day-ahead oil futures volatility. Moreover, we further find that the improved results of this model remain significant over medium-term and long-term horizons and can also outperform a simple combination forecast.

Suggested Citation

  • Feng Ma & Chao Liang & Qing Zeng & Haibo Li, 2021. "Jumps and oil futures volatility forecasting: a new insight," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 853-863, May.
  • Handle: RePEc:taf:quantf:v:21:y:2021:i:5:p:853-863
    DOI: 10.1080/14697688.2020.1805505
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2020.1805505
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2020.1805505?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. Liu, Shan & Li, Ziwei, 2023. "Macroeconomic attention and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 57(C).
    2. Wu, Nan & Wen, Fenghua & Gong, Xu, 2022. "Marionettes behind co-movement of commodity prices: Roles of speculative and hedging activities," Energy Economics, Elsevier, vol. 115(C).
    3. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
    5. Khan, Nasir & Saleem, Asima & Ozkan, Oktay, 2023. "Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: Evidence from novel non-parametric quantile causality approach," Resources Policy, Elsevier, vol. 81(C).
    6. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    7. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
    8. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Liu, Zhichao & Xu, Xiulian & Cheng, Ya & Xie, Xuan, 2023. "Geopolitical risk of oil export and import countries and oil futures volatility: Evidence from dynamic model average methods," Finance Research Letters, Elsevier, vol. 54(C).
    10. Wang, Jiqian & He, Xiaofeng & Ma, Feng & Li, Pan, 2022. "Uncertainty and oil volatility: Evidence from shrinkage method," Resources Policy, Elsevier, vol. 75(C).
    11. Su, Yuandong & Liang, Chao & Zhang, Li & Zeng, Qing, 2022. "Uncover the response of the U.S grain commodity market on El Niño–Southern Oscillation," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 98-112.
    12. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
    13. Ghani, Maria & Guo, Qiang & Ma, Feng & Li, Tao, 2022. "Forecasting Pakistan stock market volatility: Evidence from economic variables and the uncertainty index," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 1180-1189.
    14. Gongyue Jiang & Gaoxiu Qiao & Feng Ma & Lu Wang, 2022. "Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1518-1548, August.
    15. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    16. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    17. Lu, Xinjie & Su, Yuandong & Huang, Dengshi, 2023. "Chinese agricultural futures volatility: New insights from potential domestic and global predictors," International Review of Financial Analysis, Elsevier, vol. 89(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:quantf:v:21:y:2021:i:5:p:853-863. 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/RQUF20 .

    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.