IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v46y2026i5p931-951.html

The Role of Price‐Volatility Cojumps in Volatility Forecasting

Author

Listed:
  • Kefu Liao

Abstract

This paper investigates whether simultaneous jumps in prices and volatility improve volatility forecasting. Using up‐to‐date high‐frequency S&P 500 and VIX data, we identify price‐volatility cojumps at the intraday granularity and construct upside, downside, and asymmetric measures. Embedding these into the Heterogeneous Autoregressive (HAR) model, we provide new empirical evidence that downside cojumps increase future volatility, upside cojumps reduce volatility. Out‐of‐sample analysis further shows that incorporating these impacts of cojumps significantly enhances HAR model forecasting performance. Moreover, our results reveal that recent price jumps become important predictors of volatility when accompanied by simultaneous volatility jumps, an effect not previously documented in the literature. Finally, we also document the economic interpretation, policy implications, and economic value of price‐volatility cojumps.

Suggested Citation

  • Kefu Liao, 2026. "The Role of Price‐Volatility Cojumps in Volatility Forecasting," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(5), pages 931-951, May.
  • Handle: RePEc:wly:jfutmk:v:46:y:2026:i:5:p:931-951
    DOI: 10.1002/fut.70091
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/fut.70091
    Download Restriction: no

    File URL: https://libkey.io/10.1002/fut.70091?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
    ---><---

    References listed on IDEAS

    as
    1. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    2. Ellwanger, Reinhard & Snudden, Stephen, 2025. "Putting VAR forecasts of the real price of crude oil to the test," Finance Research Letters, Elsevier, vol. 77(C).
    3. Seitz, Franz & Baumann, Ursel & Albuquerque, Bruno, 2015. "The information content of money and credit for US activity," Working Paper Series 1803, European Central Bank.
    4. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    5. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    6. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    7. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    8. Simon Hirsch, 2025. "Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting," Papers 2504.02518, arXiv.org, revised Apr 2026.
    9. Rodney Edvinsson & Sune Karlsson & Pär Österholm, 2025. "Does money growth predict inflation in Sweden? Evidence from vector autoregressions using four centuries of data," Empirical Economics, Springer, vol. 68(4), pages 1613-1635, April.
    10. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2016. "Research Needs and Challenges in the FEW System: Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems," Center for Agricultural and Rural Development (CARD) Publications 16-wp563, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    11. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    12. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    13. David T. Frazier & Donald S. Poskitt, 2025. "Sequential Scoring Rule Evaluation for Forecast Method Selection," Papers 2505.09090, arXiv.org.
    14. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    15. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    16. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    17. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    18. Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca, 2025. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," International Journal of Forecasting, Elsevier, vol. 41(1), pages 361-376.
    19. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    20. Franses, Philip Hans, 2016. "A note on the Mean Absolute Scaled Error," International Journal of Forecasting, Elsevier, vol. 32(1), pages 20-22.

    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:wly:jfutmk:v:46:y:2026:i:5:p:931-951. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    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.