IDEAS home Printed from https://ideas.repec.org/p/ags/nccc24/379012.html

The Economic Value of Intraday Data in Hedging Commodity Spot Prices

Author

Listed:
  • Wu, Shujie
  • Huang, Joshua
  • Serra, Teresa

Abstract

This article shows how high-frequency market data relates to low frequency events by examining the economic value of using intraday data to hedge commodity spot prices in the futures market. We use the realized minimum-variance hedging ratio (RMVHR) framework, which depends on the realized futures-cash covariance matrix forecast. We focus on the crude oil crack and soybean crush industries and consider both multiple and single-commodity portfolios, as well as different forecast strategies based on intraday data. We use the Naïve hedging ratio as the benchmark to investigate the performance of intraday data-based hedging models. Our results suggest that for each portfolio considered, there is usually one intraday data-based hedging strategy that outperforms the Naïve. Superior performance, however, is not always statistically significant, for the crack industry. Our estimates place the advantage of using intraday data between $7,155.00 and $287.50 per contract and year on average, with these values representing the decline in the portfolio’s standard deviation achieved through hedging. This points at a promising path to improving the performance of hedging in the commodity space based on intraday data.

Suggested Citation

  • Wu, Shujie & Huang, Joshua & Serra, Teresa, 2024. "The Economic Value of Intraday Data in Hedging Commodity Spot Prices," 2024 Conference, April 22-23, 2024, St. Louis, Missouri 379012, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  • Handle: RePEc:ags:nccc24:379012
    DOI: 10.22004/ag.econ.379012
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/379012/files/Wu_Huang_Serra_NCCC-134_2024.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.379012?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. Stanislav Anatolyev & Nikita Kobotaev, 2018. "Modeling and forecasting realized covariance matrices with accounting for leverage," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 114-139, February.
    2. Xiaoyang Wang & Philip Garcia & Scott H. Irwin, 2014. "The Behavior of Bid-Ask Spreads in the Electronically-Traded Corn Futures Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(2), pages 557-577.
    3. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    4. Alexander, Carol & Prokopczuk, Marcel & Sumawong, Anannit, 2013. "The (de)merits of minimum-variance hedging: Application to the crack spread," Energy Economics, Elsevier, vol. 36(C), pages 698-707.
    5. Aaron Smith, 2005. "Partially overlapping time series: a new model for volatility dynamics in commodity futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 405-422.
    6. Manabu Asai & Michael McAleer, 2017. "The impact of jumps and leverage in forecasting covolatility," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 638-650, October.
    7. Main, Scott & Irwin, Scott H. & Sanders, Dwight R. & Smith, Aaron, 2018. "Financialization and the returns to commodity investments," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 22-28.
    8. Robert A. Collins, 2000. "The risk management effectiveness of multivariate hedging models in the U.S. soy complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(2), pages 189-204, February.
    9. Symitsi, Efthymia & Symeonidis, Lazaros & Kourtis, Apostolos & Markellos, Raphael, 2018. "Covariance forecasting in equity markets," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 153-168.
    10. Anderson, Ronald W & Danthine, Jean-Pierre, 1980. "Hedging and Joint Production: Theory and Illustrations," Journal of Finance, American Finance Association, vol. 35(2), pages 487-498, May.
    11. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    12. Yudong Wang & Chongfeng Wu & Li Yang, 2015. "Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?," Management Science, INFORMS, vol. 61(12), pages 2870-2889, December.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Aaron Smith, 2005. "Partially overlapping time series: a new model for volatility dynamics in commodity futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 405-422, March.
    15. Dah‐Nein Tzang & Raymond M. Leuthold, 1990. "Hedge ratios under inherent risk reduction in a commodity complex," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 10(5), pages 497-504, October.
    16. Willems, Bert & Morbee, Joris, 2010. "Market completeness: How options affect hedging and investments in the electricity sector," Energy Economics, Elsevier, vol. 32(4), pages 786-795, July.
    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. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    2. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2017. "Realized stochastic volatility with general asymmetry and long memory," Journal of Econometrics, Elsevier, vol. 199(2), pages 202-212.
    3. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    4. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    5. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    6. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
    7. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    8. Goswami, Alankrita & Karali, Berna & Adjemian, Michael K., 2023. "Hedging with futures during nonconvergence in commodity markets," Journal of Commodity Markets, Elsevier, vol. 32(C).
    9. Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, vol. 55(PA).
    10. Yan Hu & Jian Ni, 2024. "A deep learning‐based financial hedging approach for the effective management of commodity risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 879-900, June.
    11. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    12. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    13. Luo, Jiawen & Cepni, Oguzhan & Demirer, Riza & Gupta, Rangan, 2025. "Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies," Journal of Empirical Finance, Elsevier, vol. 81(C).
    14. Cao, Min & Conlon, Thomas, 2023. "Composite jet fuel cross-hedging," Journal of Commodity Markets, Elsevier, vol. 30(C).
    15. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.
    16. Dahlgran, Roger A., 2011. "Hedging and Cash Flow Risk in Ethanol Refining," 2011 Conference, April 18-19, 2011, St. Louis, Missouri 285337, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    17. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    18. Zhang, Chao & Pu, Xingyue & Cucuringu, Mihai & Dong, Xiaowen, 2025. "Forecasting realized volatility with spillover effects: Perspectives from graph neural networks," International Journal of Forecasting, Elsevier, vol. 41(1), pages 377-397.
    19. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    20. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.

    More about this item

    Keywords

    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ags:nccc24:379012. 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: AgEcon Search (email available below). General contact details of provider: http://www.farmdoc.illinois.edu/nccc134/ .

    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.