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Volatility Dynamics and Seasonality in Energy Prices: Implications for Crack-Spread Price Risk

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  • Hiroaki Suenaga
  • Aaron Smith

Abstract

We examine the volatility dynamics of three major petroleum commodities traded on the NYMEX: crude oil, unleaded gasoline, and heating oil. Using the partially overlapping time-series (POTS) framework of Smith (2005), we model jointly all futures contracts with delivery dates up to a year into the future and extract information from these prices about the persistence of market shocks. The model depicts highly nonlinear volatility dynamics that are consistent with the observed seasonality in demand and storage of the three commodities. Specifically, volatility of the three commodity prices exhibits time-to-delivery effects and substantial seasonality, yet their patterns vary systematically by contract delivery month. The conditional variance and correlation across the three commodities also vary over time. High price volatility of near-delivery contracts and their low correlation with concurrently traded distant contracts imply high short-horizon price risk for an unhedged position in the calendar or crack spread. Price risk at the one-year horizon is much lower than short-horizon risk in all seasons and for all positions, but it is still substantial in magnitude for crack-spread positions. Crack-spread hedgers ignore nearby high-season price risk at their peril, but they would also be remiss to ignore the long horizon.

Suggested Citation

  • Hiroaki Suenaga & Aaron Smith, 2011. "Volatility Dynamics and Seasonality in Energy Prices: Implications for Crack-Spread Price Risk," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 27-58.
  • Handle: RePEc:aen:journl:32-3-a02
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    Cited by:

    1. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    2. Suenaga, Hiroaki, 2013. "Measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 53-66.
    3. Schnake, Kristin N. & Karali, Berna & Dorfman, Jeffrey H., 2012. "The Informational Content of Distant-Delivery Futures Contracts," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), August.
    4. Hahn, Warren J. & DiLellio, James A. & Dyer, James S., 2014. "What do market-calibrated stochastic processes indicate about the long-term price of crude oil?," Energy Economics, Elsevier, vol. 44(C), pages 212-221.

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    JEL classification:

    • F0 - International Economics - - General

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