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Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective

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  • Wang, Minggang
  • Chen, Ying
  • Tian, Lixin
  • Jiang, Shumin
  • Tian, Zihao
  • Du, Ruijin

Abstract

The directed weighted networks of international crude oil and gasoline price were built in the different fluctuation periods. And then the evolution law of the new nodes in the prices networks was analyzed. The results indicated that the cumulative times of the new nodes that appeared in the crude oil and gasoline prices networks were not random but exhibited a high linear growth trend, which revealed the linear characteristics of the accumulation time of abnormal points that appeared in the process of oil price fluctuations. Based on the node strength, the calculation formula of the network similarity between the crude oil and gasoline price networks was designed, and the interdependence between the crude oil and gasoline price fluctuations was calculated, the results indicated that there was a strong interdependence between crude oil and gasoline prices in stable fluctuation periods, but the degree of dependence was significantly reduced in sharp fluctuation periods. The strength of nodes and their strength distribution, weighted clustering coefficient, and average shortest paths of the price network in different periods were calculated. The fluctuation characteristics in different periods were comparatively analyzed. The core fluctuation status and the conversion relationship between them in different periods were revealed. Finally, the important modes of price fluctuations of crude oil and gasoline were identified and the distribution characteristics of the time these modes appeared were studied.

Suggested Citation

  • Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:109-127
    DOI: 10.1016/j.apenergy.2016.05.013
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    References listed on IDEAS

    as
    1. Coleman, Les, 2012. "Explaining crude oil prices using fundamental measures," Energy Policy, Elsevier, vol. 40(C), pages 318-324.
    2. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    3. Nazlioglu, Saban & Soytas, Ugur & Gupta, Rangan, 2015. "Oil prices and financial stress: A volatility spillover analysis," Energy Policy, Elsevier, vol. 82(C), pages 278-288.
    4. 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.
    5. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    6. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    7. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
    8. 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.
    9. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    10. 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.
    11. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    12. Narayan, Paresh Kumar & Narayan, Seema & Popp, Stephan, 2011. "Investigating price clustering in the oil futures market," Applied Energy, Elsevier, vol. 88(1), pages 397-402, January.
    13. Gao, Xiangyun & An, Haizhong & Fang, Wei & Li, Huajiao & Sun, Xiaoqi, 2014. "The transmission of fluctuant patterns of the forex burden based on international crude oil prices," Energy, Elsevier, vol. 73(C), pages 380-386.
    14. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    15. 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.
    16. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    17. Dias, José G. & Ramos, Sofia B., 2014. "Energy price dynamics in the U.S. market. Insights from a heterogeneous multi-regime framework," Energy, Elsevier, vol. 68(C), pages 327-336.
    18. Atil, Ahmed & Lahiani, Amine & Nguyen, Duc Khuong, 2014. "Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices," Energy Policy, Elsevier, vol. 65(C), pages 567-573.
    19. Chai, Jian & Guo, Ju-E. & Meng, Lei & Wang, Shou-Yang, 2011. "Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model," Energy Policy, Elsevier, vol. 39(12), pages 8022-8036.
    20. Zhang, Yue-Jun & Wang, Zi-Yi, 2013. "Investigating the price discovery and risk transfer functions in the crude oil and gasoline futures markets: Some empirical evidence," Applied Energy, Elsevier, vol. 104(C), pages 220-228.
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