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Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach

Author

Listed:
  • Bangzhu Zhu
  • Chunzhuo Wan
  • Ping Wang
  • Julien Chevallier

Abstract

This paper proposes a novel hybrid multiscale decomposition and bootstrap approach for carbon price interval forecasting, aiming to overcome the limitations of traditional carbon price point forecasting. The original carbon price is decomposed into simple modes using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and various bootstrap methods are applied to perform a random sampling with a replacement on each mode, generating pseudo datasets forecasted using extreme gradient boosting (XGB). The forecasting values of all modes are then integrated into the original carbon price interval forecasting values. The empirical results, based on samples from China's Guangdong and Hubei carbon markets, provide compelling evidence of the effectiveness of our model. It achieves higher forecasting accuracy, higher interval coverage, and narrower forecasting intervals than currently popular prediction models, instilling confidence in its potential to enhance carbon price forecasting.

Suggested Citation

  • Bangzhu Zhu & Chunzhuo Wan & Ping Wang & Julien Chevallier, 2025. "Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 376-390, March.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:2:p:376-390
    DOI: 10.1002/for.3199
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    References listed on IDEAS

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    1. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
    2. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    3. Karsten Schweikert, 2021. "Bootstrap Confidence Intervals and Hypothesis Testing for Market Information Shares [Price Discovery and Common Factor Models]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 934-959.
    4. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    5. 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).
    6. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    7. Eugenia Sanin, María & Violante, Francesco & Mansanet-Bataller, María, 2015. "Understanding volatility dynamics in the EU-ETS market," Energy Policy, Elsevier, vol. 82(C), pages 321-331.
    8. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    9. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
    10. Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
    11. 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).
    12. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    13. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
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