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Coal Price Forecasting and Structural Analysis in China

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  • Xiaopeng Guo
  • Jiaxing Shi
  • Dongfang Ren

Abstract

Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI), and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR) model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE) was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions.

Suggested Citation

  • Xiaopeng Guo & Jiaxing Shi & Dongfang Ren, 2016. "Coal Price Forecasting and Structural Analysis in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-7, October.
    • Handle: RePEc:hin:jnddns:1256168
    DOI: 10.1155/2016/1256168
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    Cited by:

    1. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    2. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    3. Qingru Sun & Xiangyun Gao & Shaobo Wen & Sida Feng & Ze Wang, 2019. "Modeling the impulse response complex network for studying the fluctuation transmission of price indices," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 835-858, December.
    4. Jingna Kou & Fengjun Sun & Wei Li & Jie Jin, 2022. "Could China Declare a “Coal Phase-Out”? An Evolutionary Game and Empirical Analysis Involving the Government, Enterprises, and the Public," Energies, MDPI, vol. 15(2), pages 1-24, January.
    5. Piotr Bórawski & Aneta Bełdycka-Bórawska & Lisa Holden, 2023. "Changes in the Polish Coal Sector Economic Situation with the Background of the European Union Energy Security and Eco-Efficiency Policy," Energies, MDPI, vol. 16(2), pages 1-17, January.
    6. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    7. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
    8. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    9. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).

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