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The Informational Content of Inventory Announcements: Intraday Evidence from Crude Oil Futures Market

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  • Ye, Shiyu
  • Karali, Berna

Abstract

This paper examines the behavior of intraday crude oil futures return and volatility and how they respond to weekly inventory announcements by the American Petroleum Institute (API) and Energy Information Administration (EIA). The informational content of API reports is measured relative to market analysts' expectations collected by Reuters, whereas that of EIA reports is measured relative to API reports. Results suggest that unexpected inventory changes in both API and EIA reports exert an immediate inverse impact on returns and a positive impact on volatility; but the duration and magnitude of EIA inventory shocks are longer and larger, with the largest impact observed when Reuters and API both err on the same side. While there are no instant asymmetric return responses to positive and negative API shocks, the return and volatility responses to cross-commodity inventory shocks in EIA reports exhibit asymmetry.
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  • Ye, Shiyu & Karali, Berna, 2015. "The Informational Content of Inventory Announcements: Intraday Evidence from Crude Oil Futures Market," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205595, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:205595
    DOI: 10.22004/ag.econ.205595
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    Cited by:

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    2. Olivier Rousse & Benoît Sévi, 2016. "Informed Trading in Oil-Futures Market," Working Papers hal-01410093, HAL.
    3. Wen, Zhuzhu & Gong, Xu & Ma, Diandian & Xu, Yahua, 2021. "Intraday momentum and return predictability: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 95(C), pages 374-384.
    4. Ayadi, Mohamed A. & Ben Omrane, Walid & Lazrak, Skander & Yan, Xusheng, 2020. "OPEC production decisions, macroeconomic news, and volatility in the Canadian currency and oil markets," Finance Research Letters, Elsevier, vol. 37(C).
    5. Plante, Michael & Dhaliwal, Navi, 2017. "Inventory shocks and the oil–ethanol–grain price nexus," Economics Letters, Elsevier, vol. 156(C), pages 58-60.
    6. Louis H. Ederington, Fang Lin, Scott C. Linn, and Lisa (Zongfei) Yang, 2019. "EIA Storage Announcements, Analyst Storage Forecasts, and Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    7. Diaz-Rainey, Ivan & Roberts, Helen & Lont, David H., 2017. "Crude inventory accounting and speculation in the physical oil market," Energy Economics, Elsevier, vol. 66(C), pages 508-522.
    8. Wang, Cheng & Bouri, Elie & Xu, Yahua & Zhang, Dingsheng, 2023. "Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks," Energy Economics, Elsevier, vol. 127(PB).
    9. Lutz Kilian & Xiaoqing Zhou, 2023. "The Econometrics of Oil Market VAR Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 65-95, Emerald Group Publishing Limited.
    10. Zhuzhu Wen & Ivan Indriawan & Donald Lien & Yahua Xu, 2023. "Intraday Return Predictability in the Crude Oil Market: The Role of EIA Inventory Announcements," The Energy Journal, , vol. 44(5), pages 149-172, September.
    11. Tarek Chebbi & Waleed Hmedat, 2024. "Inventory information arrival and the crude oil futures market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1513-1533, April.
    12. Marcel Prokopczuk & Chardin Wese Simen & Robert Wichmann, 2021. "The Natural Gas Announcement Day Puzzle," The Energy Journal, , vol. 42(2), pages 91-112, March.
    13. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    14. Li, Wenhui & Zhu, Qi & Wen, Fenghua & Nor, Normaziah Mohd, 2022. "The evolution of day-of-the-week and the implications in crude oil market," Energy Economics, Elsevier, vol. 106(C).
    15. Olivier Rousse & Benoît Sévi, 2017. "Informed Trading in Oil-Futures Market," Working Papers hal-01460186, HAL.
    16. Do, Hung Xuan & Nguyen, Quan M.P. & Nepal, Rabindra & Smyth, Russell, 2021. "When Pep comes calling, the oil market answers: The effect of football player transfer movements on abnormal fluctuations in oil price futures," Energy Economics, Elsevier, vol. 100(C).
    17. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    18. Tian, Xiao & Duong, Huu Nhan & Kalev, Petko S., 2019. "Information content of the limit order book for crude oil futures price volatility," Energy Economics, Elsevier, vol. 81(C), pages 584-597.
    19. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.
    20. Ahmad, Wasim & Prakash, Ravi & Uddin, Gazi Salah & Chahal, Rishman Jot Kaur & Rahman, Md. Lutfur & Dutta, Anupam, 2020. "On the intraday dynamics of oil price and exchange rate: What can we learn from China and India?," Energy Economics, Elsevier, vol. 91(C).
    21. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
    22. Lee, Chien-Chiang & Wang, Chih-Wei & Ho, Shan-Ju & Wu, Ting-Pin, 2021. "The impact of natural disaster on energy consumption: International evidence," Energy Economics, Elsevier, vol. 97(C).
    23. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).

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    More about this item

    Keywords

    Demand and Price Analysis;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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