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Testing the evolution of crude oil market efficiency: Data have the conn

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  • Zhang, Bing
  • Li, Xiao-Ming
  • He, Fei

Abstract

Utilising a time-varying GAR (1)-TGARCH (1,1) model with different frequency data, we investigate the weak-form efficiency of major global crude oil spot markets in Europe, the US, the UAE and China for the period from December 2001 to August 2013. Our empirical results with weekly data indicate that all four markets have reached efficiency with few brief inefficient periods during the past decade, whereas the daily crude oil returns series suggest intermittent and inconsistent efficiency. We argue that the weekly Friday series fit the data better than the average series in autocorrelation tests. The evidence suggests that all four markets exhibit asymmetries in return-volatility reactions to different information shocks and that they react more strongly to bad news than to good news. The 2008 financial crisis has significantly affected the efficiency of oil markets. Furthermore, a comovement phenomenon and volatility spillover effects exist among the oil markets. Policy recommendations consistent with our empirical results are proposed, which address three issues: implementing prudential regulations, establishing an Asian pricing centre and improving transparency in crude oil spot markets.

Suggested Citation

  • Zhang, Bing & Li, Xiao-Ming & He, Fei, 2014. "Testing the evolution of crude oil market efficiency: Data have the conn," Energy Policy, Elsevier, vol. 68(C), pages 39-52.
    • Handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:39-52
    DOI: 10.1016/j.enpol.2013.12.052
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    References listed on IDEAS

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    Cited by:

    1. Tiwari, Aviral Kumar & Kumar, Satish & Pathak, Rajesh & Roubaud, David, 2019. "Testing the oil price efficiency using various measures of long-range dependence," Energy Economics, Elsevier, vol. 84(C).
    2. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    3. Adewuyi, Adeolu O. & Wahab, Bashir A. & Adeboye, Olusegun S., 2020. "Stationarity of prices of precious and industrial metals using recent unit root methods: Implications for markets’ efficiency," Resources Policy, Elsevier, vol. 65(C).
    4. Liu, Xueyong & An, Haizhong & Li, Huajiao & Chen, Zhihua & Feng, Sida & Wen, Shaobo, 2017. "Features of spillover networks in international financial markets: Evidence from the G20 countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 265-278.
    5. Górska, Anna & Krawiec, Monika, 2017. "Analiza efektywności informacyjnej w formie słabej na rynkach „soft commodities” z wykorzystaniem wybranych testów statystycznych," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 17(32, Part ), September.
    6. Alvarez-Ramirez, J. & Alvarez, J. & Rodríguez, E., 2015. "Asymmetric long-term autocorrelations in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 330-341.
    7. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.

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