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Identifying the peak point of systemic risk in international crude oil importing trade

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  • Du, Ruijin
  • Dong, Gaogao
  • Tian, Lixin
  • Wang, Yougui
  • Zhao, Longfeng
  • Zhang, Xin
  • Vilela, André L.M.
  • Stanley, H. Eugene

Abstract

The fluctuations of international crude oil markets have caused significant attention around the world and aroused strong interest in the forecasting of the systemic risk in crude oil trade. Based on the oil imported values data of 34 major oil-importing countries from January 2005 to June 2017, we calculate the cross-correlation functions of time lags and construct a sequence of time-evolving oil import correlation networks according to the similarities between countries. The probability distribution of time lag shows that the time lag effect is not sensitive to positive correlations, but obvious for negative correlations. There is a longer time-lag effect in the years when positive correlations are stronger. Further, we use a percolation analysis to quantify the structural change in the correlation network. The key result is that abrupt percolation transition is leading spikes in systemic risk with advance of 3–11 months suggesting that this event could function as an alarm. Therefore, percolation transition in the correlation network of oil-importing countries can be used as a means to estimate signals about future systemic risk. The methodology and results presented in this paper bring a fresh perspective to the study of systemic risk in crude oil importing trade, and they facilitate risk early-warning research in other energy systems that also have interactions among their elements.

Suggested Citation

  • Du, Ruijin & Dong, Gaogao & Tian, Lixin & Wang, Yougui & Zhao, Longfeng & Zhang, Xin & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Identifying the peak point of systemic risk in international crude oil importing trade," Energy, Elsevier, vol. 176(C), pages 281-291.
  • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:281-291
    DOI: 10.1016/j.energy.2019.03.127
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    as
    1. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2010. "The evolution of the world trade web: a weighted-network analysis," Journal of Evolutionary Economics, Springer, vol. 20(4), pages 479-514, August.
    2. Pukthuanthong, Kuntara & Roll, Richard, 2009. "Global market integration: An alternative measure and its application," Journal of Financial Economics, Elsevier, vol. 94(2), pages 214-232, November.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    7. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    8. Zhang, Hai-Ying & Ji, Qiang & Fan, Ying, 2014. "Competition, transmission and pattern evolution: A network analysis of global oil trade," Energy Policy, Elsevier, vol. 73(C), pages 312-322.
    9. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    10. Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    11. Peter H. Westfall, 2014. "Kurtosis as Peakedness, 1905-2014. R.I.P," The American Statistician, Taylor & Francis Journals, vol. 68(3), pages 191-195, April.
    12. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    13. James D. Hamilton, 2011. "Historical Oil Shocks," NBER Working Papers 16790, National Bureau of Economic Research, Inc.
    14. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    15. Výrost, Tomas & Lyócsa, Štefan & Baumöhl, Eduard, 2019. "Network-based asset allocation strategies," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 516-536.
    16. An, Haizhong & Zhong, Weiqiong & Chen, Yurong & Li, Huajiao & Gao, Xiangyun, 2014. "Features and evolution of international crude oil trade relationships: A trading-based network analysis," Energy, Elsevier, vol. 74(C), pages 254-259.
    17. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    18. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    19. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    20. Ruijin Du & Gaogao Dong & Lixin Tian & Minggang Wang & Guochang Fang & Shuai Shao, 2016. "Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.
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    7. N. Wei & W. -J. Xie & W. -X. Zhou, 2021. "Robustness of the international oil trade network under targeted attacks to economies," Papers 2101.10679, arXiv.org, revised Jan 2021.
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