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Time-varying Granger causality tests for applications in global crude oil markets

Author Info

Listed author(s):
  • Lu, Feng-bin
  • Hong, Yong-miao
  • Wang, Shou-yang
  • Lai, Kin-keung
  • Liu, John
Registered author(s):

    Abstract

    This paper proposes time-varying Granger causality tests based on the tests developed by Hong (2001) and two dynamic correlation estimators (i.e., rolling correlation and dynamic conditional correlation multivariate GARCH), here called the rolling Hong and DCC-MGARCH Hong tests, respectively. The proposed tests are used to examine time-varying information spillover among global crude oil markets. The results provide empirical evidence of time-varying information spillover. In particular, the instantaneous causal effects of Dubai and Tapis crudes on Brent and WTI become stronger when a major event or events occur in major oil-producing countries. Such events include the Iraq War in March 2003, OPEC's announcement of a record production cut in December 2008, and the Libyan civil war in early 2011. And consistent with previous studies, WTI and Brent play dominant roles in global crude markets. Impulse response analysis shows that market information has a positive influence on the spillover effect in global crude oil markets. Moreover, the DCC-MGARCH Hong test consistently leads the rolling Hong test, which indicates that the former performs better.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0140988314000048
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    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 42 (2014)
    Issue (Month): C ()
    Pages: 289-298

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    Handle: RePEc:eee:eneeco:v:42:y:2014:i:c:p:289-298
    DOI: 10.1016/j.eneco.2014.01.002
    Contact details of provider: Web page: http://www.elsevier.com/locate/eneco

    Keywords: Time-varying Granger causality; Information spillover; Rolling correlation; DCC-MGARCH; Crude oil market;

    Find related papers by JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • F30 - International Economics - - International Finance - - - General

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    Cited by:

    1. Smyth, Russell & Narayan, Paresh Kumar, 2015. "Applied econometrics and implications for energy economics research," Energy Economics, Elsevier, vol. 50(C), pages 351-358.
    2. Eduard Baumohl & Evzen Kocenda & Stefan Lyocsa & Tomas Vyrost, 2016. "Networks of volatility spillovers among stock markets," KIER Working Papers 941, Kyoto University, Institute of Economic Research.
    3. Saafi Sami & Farhat Abdeljelil & Haj Mohamed Meriem Bel, 2015. "Testing the relationships between shadow economy and unemployment: empirical evidence from linear and nonlinear tests," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 585-608, December.
    4. Chang, Chun-Ping & Lee, Chien-Chiang, 2015. "Do oil spot and futures prices move together?," Energy Economics, Elsevier, vol. 50(C), pages 379-390.
    5. Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
    6. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    7. Kuruppuarachchi, Duminda & Premachandra, I.M., 2016. "Information spillover dynamics of the energy futures market sector: A novel common factor approach," Energy Economics, Elsevier, vol. 57(C), pages 277-294.
    8. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    9. Stefan Lyocsa & Tomas Vyrost & Eduard Baumohl, 2015. "Return spillovers around the globe: A network approach," Papers 1507.06242, arXiv.org, revised Nov 2015.

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