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Multi-fractal fluctuation features of thermal power coal price in China

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  • Zhao, Zhen-yu
  • Zhu, Jiang
  • Xia, Bo

Abstract

Within the current energy structure in China, coal consumption accounts for 60% of the total energy consumption. Understanding the features of coal prices is important to the energy industry as the prices have a profound impact on the energy development, especially to the thermal power business. This paper uses the multi-fractal theory introduced from financial price field to examine the fluctuations of thermal power coal price by multi-fractal detrended fluctuation analysis (MFDFA). A steam coal Free-on-Board (FOB) price in Qinhuangdao Port, China's largest port of coal storage and transportation, was chosen to represent the thermal power coal price and to reflect the price fluctuation. The analysis shows that the thermal power coal price has multi-fractal features. Consequently, a Quarterly Fluctuation Index (QFI) for thermal power coal price was proposed to forecast the coal price caused by market fluctuation as the fractal model based on QFI had a better forecasting ability when the prices fluctuate wildly. Especially, the QFI can help both government and enterprises to improve their capabilities to manage the fluctuation risks. This study also provides a useful reference to understand the multi-fractal fluctuation features in other energy prices.

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  • Zhao, Zhen-yu & Zhu, Jiang & Xia, Bo, 2016. "Multi-fractal fluctuation features of thermal power coal price in China," Energy, Elsevier, vol. 117(P1), pages 10-18.
    • Handle: RePEc:eee:energy:v:117:y:2016:i:p1:p:10-18
    DOI: 10.1016/j.energy.2016.10.081
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    as
    1. Scott, K. Rebecca, 2015. "Demand and price uncertainty: Rational habits in international gasoline demand," Energy, Elsevier, vol. 79(C), pages 40-49.
    2. Jarvis, Darryl S.L. & Sovacool, Benjamin K., 2011. "Conceptualizing and evaluating best practices in electricity and water regulatory governance," Energy, Elsevier, vol. 36(7), pages 4340-4352.
    3. Liu, Ming-Hua & Margaritis, Dimitris & Zhang, Yang, 2013. "Market-driven coal prices and state-administered electricity prices in China," Energy Economics, Elsevier, vol. 40(C), pages 167-175.
    4. Banovac, Eraldo & Glavić, Mevludin & Tešnjak, Sejid, 2009. "Establishing an efficient regulatory mechanism—Prerequisite for successful energy activities regulation," Energy, Elsevier, vol. 34(2), pages 178-189.
    5. Mali, Provash & Mukhopadhyay, Amitabha, 2014. "Multifractal characterization of gold market: A multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 361-372.
    6. Lin, Boqiang & Wesseh, Presley K., 2013. "What causes price volatility and regime shifts in the natural gas market," Energy, Elsevier, vol. 55(C), pages 553-563.
    7. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    8. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    9. Xu, Baoping & Zhou, Shaoxiang & Hao, Lin, 2015. "Approach and practices of district energy planning to achieve low carbon outcomes in China," Energy Policy, Elsevier, vol. 83(C), pages 109-122.
    10. Xiong, Gang & Yu, Wenxian & Zhang, Shuning, 2015. "Time-singularity multifractal spectrum distribution based on detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 351-366.
    11. Sun, Mei & Zhang, Pei-Pei & Shan, Tian-Hua & Fang, Cui-Cui & Wang, Xiao-Fang & Tian, Li-Xin, 2012. "Research on the evolution model of an energy supply–demand network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4506-4516.
    12. repec:ipg:wpaper:2014-589 is not listed on IDEAS
    13. Mandelbrot, Benoit B., 1990. "New “anomalous” multiplicative multifractals: Left sided ƒ(α) and the modelling of DLA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 168(1), pages 95-111.
    14. Flores-Márquez, E.L. & Ramírez-Rojas, A. & Telesca, L., 2015. "Multifractal detrended fluctuation analysis of earthquake magnitude series of Mexican South Pacific Region," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 1106-1114.
    15. Zhang, Chen & Ni, Zhiwei & Ni, Liping, 2015. "Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 114-123.
    16. Özbek, Levent & Özlale, Ümit, 2010. "Analysis of real oil prices via trend-cycle decomposition," Energy Policy, Elsevier, vol. 38(7), pages 3676-3683, July.
    17. 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.
    18. Shahbaz, Muhammad & Khraief, Naceur & Mahalik, Mantu Kumar & Zaman, Khair Uz, 2014. "Are fluctuations in natural gas consumption per capita transitory? Evidence from time series and panel unit root tests," Energy, Elsevier, vol. 78(C), pages 183-195.
    19. He, Yongxiu & Liu, Yangyang & Wang, Jianhui & Xia, Tian & Zhao, Yushan, 2014. "Low-carbon-oriented dynamic optimization of residential energy pricing in China," Energy, Elsevier, vol. 66(C), pages 610-623.
    20. 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.
    Full references (including those not matched with items on IDEAS)

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