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Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

Author

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  • Huan Chen

    (Energy Development and Environmental Protection Strategy Research Center, Jiangsu University, Zhenjiang 212013, China)

  • Lixin Tian

    (Energy Development and Environmental Protection Strategy Research Center, Jiangsu University, Zhenjiang 212013, China
    School of Mathematical Science, Nanjing Normal University, Nanjing 210042, China)

  • Minggang Wang

    (School of Mathematical Science, Nanjing Normal University, Nanjing 210042, China
    Department of Mathematics, Nanjing Normal University Taizhou College, Taizhou 225300, China)

  • Zaili Zhen

    (Energy Development and Environmental Protection Strategy Research Center, Jiangsu University, Zhenjiang 212013, China)

Abstract

Heating oil is an extremely important heating fuel to consumers in northeastern United States. This paper studies the fluctuations law and dynamic behavior of heating oil spot and futures prices by setting up their complex network models based on the data of America in recent 30 years. Firstly, modes are defined by the method of coarse graining, the spot price fluctuation network of heating oil (HSPFN) and its futures price fluctuation network (HFPFN) in different periods are established to analyze the transformation characteristics between the modes. Secondly, several indicators are investigated: average path length, node strength and strength distribution, betweeness, etc. In addition, a function is established to measure and analyze the network similarity. The results show the cumulative time of new nodes appearing in either spot or futures price network is not random but exhibits a growth trend of straight line. Meanwhile, the power law distributions of spot and futures price fluctuations in different periods present regularity and complexity. Moreover, these prices are strongly correlated in stable fluctuation period but weak in the phase of sharp fluctuation. Finally, the time distribution characteristics of important modes in the networks and the evolution results of the topological properties mentioned above are obtained.

Suggested Citation

  • Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:574-:d:95419
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    Cited by:

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    5. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
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    7. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    8. Lixin Tian & Huan Chen & Zaili Zhen, 2018. "Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.

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