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A Characterization of Oil Price Behavior - Evidence from Jump Models


  • Marc Gronwald


This paper is concerned with the statistical behavior of oil prices in two ways. It, firstly, applies a combined jump GARCH in order to characterize the behavior of daily, weekly as well as monthly oil prices. Secondly, it relates its empirical results to implications of Hotelling-type resource extraction models. The empirical analysis shows that oil prices are characterized by GARCH as well as conditional jump behavior and that a considerable portion of the total variance is triggered by sudden extreme price movements. This finding implies that, first, oil price signals are not reliable and, as a consequence, both finding optimal extraction paths and decisions regarding the transmission to alternative technologies are likely to be compromised. Second, this behavior is in stark contrast to the notion of deterministic trends in the price of oil.

Suggested Citation

  • Marc Gronwald, 2011. "A Characterization of Oil Price Behavior - Evidence from Jump Models," CESifo Working Paper Series 3644, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_3644

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    References listed on IDEAS

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    Cited by:

    1. Sévi, Benoît, 2015. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Economic Modelling, Elsevier, vol. 44(C), pages 243-251.
    2. Guo, Yanfeng & Wen, Xiaoqian & Wu, Yanrui & Guo, Xiumei, 2016. "How is China's coke price related with the world oil price? The role of extreme movements," Economic Modelling, Elsevier, vol. 58(C), pages 22-33.
    3. Márcio Poletti Laurini & Roberto Baltieri Mauad & Fernando Antonio Lucena Aiube, 2016. "Multivariate Stochastic Volatility-Double Jump Model: an application for oil assets," Working Papers Series 415, Central Bank of Brazil, Research Department.
    4. Zhang, Chuanguo & Qu, Xuqin, 2015. "The effect of global oil price shocks on China's agricultural commodities," Energy Economics, Elsevier, vol. 51(C), pages 354-364.
    5. Sharma, Shahil & Escobari, Diego, 2017. "Identifying Price Bubble Periods in the Energy Sector," MPRA Paper 83355, University Library of Munich, Germany.
    6. Zhang, Chuanguo & Tu, Xiaohua, 2016. "The effect of global oil price shocks on China's metal markets," Energy Policy, Elsevier, vol. 90(C), pages 131-139.
    7. Gronwald, Marc, 2016. "Explosive oil prices," Energy Economics, Elsevier, vol. 60(C), pages 1-5.
    8. Marc Gronwald & Janina Ketterer, 2012. "What Moves the European Carbon Market? - Insights from Conditional Jump Models," CESifo Working Paper Series 3795, CESifo Group Munich.
    9. Wang, Xiao & Zhang, Chuanguo, 2014. "The impacts of global oil price shocks on China׳s fundamental industries," Energy Policy, Elsevier, vol. 68(C), pages 394-402.
    10. Asche, Frank & Dahl, Roy Endre & Oglend, Atle, 2013. "Value-at-Risk: Risk assessment for the portfolio of oil and gas producers," UiS Working Papers in Economics and Finance 2013/3, University of Stavanger.

    More about this item


    oil price; conditional jumps; GARCH; Hotelling; climate change; deterministic trend;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General


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