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Formation of Market Beliefs in the Oil Market

Author

Listed:
  • Stanislav Anatolyev
  • Sergei Seleznev
  • Veronika Selezneva

Abstract

We characterize formation of market beliefs in the oil market by providing a complete characterization of the market reaction to oil inventory surprises. We utilize the unique sequential nature of inventory announcements to identify inventory shocks. We estimate an AR-ARCH-MEM model of the joint dynamics of returns, return volatilities and trading volumes around the announcements using high frequency data on oil futures contracts. Our model (i) handles illiquidity of long maturity contracts by accounting for trading inactivity, (ii) captures time varying trading intensity, and (iii) allows for structural changes in the dynamics and responses to news over time. We show (i) uniform formation of expectations across oil futures contracts with different maturities, (ii) a strong negative relation between inventories surprises and returns, (iii) no effect on the term premium, which suggests that inventory shocks are always considered to be permanent, and (iv) differentiation in the reaction of volume by maturity. We demonstrate how our results can be used to test theories of oil price determination and contribute to the debate on the recent oil glut.

Suggested Citation

  • Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    • Handle: RePEc:cer:papers:wp619
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    References listed on IDEAS

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    Cited by:

    1. Sultan Alturki & Alexander Kurov, 2022. "Market inefficiencies surrounding energy announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 172-188, January.

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    More about this item

    Keywords

    oil market; ultra high frequency data; trading intensity; futures returns; return volatility; inventory surprises; expectation formation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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