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Spikes and crashes in the oil market

Author

Listed:
  • Sofiane Aboura

    (CEPN - Centre d'Economie de l'Université Paris Nord - UP13 - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique)

  • Julien Chevallier

    (IPAG Lab - IPAG Lab - IPAG Business School)

Abstract

Over the last three decades, advanced economies have been facing a substantial rise not only in the crude oil price, but also in the oil price volatility. Quantifying the tail risk has become a prominent issue for investors and policy makers given the repeated spikes and crashes during previous years. This article reveals the existence of a tail risk hidden in the oil market by applying, for the first time, an extreme value theory analysis with a quantile regression procedure. An empirical test is carried out on the daily West Texas Intermediate (WTI) crude oil prices from 1983 to 2013. The main results indicate that the WTI becomes extreme from a daily variation of +5.0% and −10.0%. In addition, the maximum one-day variation which should be exceeded only once per century is +23% and −33%. Finally, the tail risk is overall borne by the oil-importing countries. The main policy implication of these findings is to design policy measures that consider the existence of price-volatility thresholds above/below which the oil market becomes unstable.

Suggested Citation

  • Sofiane Aboura & Julien Chevallier, 2016. "Spikes and crashes in the oil market," Post-Print halshs-01348711, HAL.
    • Handle: RePEc:hal:journl:halshs-01348711
    DOI: 10.1016/j.ribaf.2015.07.002
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    Cited by:

    1. Lin, Boqiang & Bai, Rui, 2021. "Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective," Research in International Business and Finance, Elsevier, vol. 56(C).
    2. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
    3. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    4. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.

    More about this item

    Keywords

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    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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