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Forecasting carbon market volatility with big data

Author

Listed:
  • Bangzhu Zhu

    (Guangxi University
    Key Laboratory of Interdisciplinary Science of Statistics and Management, Education Department of Guangxi)

  • Chunzhuo Wan

    (Guilin University of Electronic Technology)

  • Ping Wang

    (Jinan University)

  • Julien Chevallier

    (4IPAG Lab, IPAG Business School
    LED, University of Paris 8)

Abstract

This paper proposes an ensemble forecasting model for carbon market volatility with structural factors and non-structural Baidu search index. Firstly, wavelet analysis is introduced into carbon price denoising for obtaining carbon market volatility. Secondly, carbon market volatility forecasting is converted into a multi-class forecasting problem. Thirdly, synthetic minority over sampling technique tomek links (SMOTETomek) is used to address the class imbalance problem. Fourthly, extreme gradient boosting (XGBoost) is used for carbon market volatility forecasting, and genetic algorithm (GA) is employed into synchronously optimize all parameters of XGBoost. Taking Guangdong and Hubei carbon markets as samples, the proposed model has higher overall forecasting performance and higher minority class forecasting performance when compared with other popular prediction models. The sensitivity analysis verifies that the proposed model is robust.

Suggested Citation

  • Bangzhu Zhu & Chunzhuo Wan & Ping Wang & Julien Chevallier, 2025. "Forecasting carbon market volatility with big data," Annals of Operations Research, Springer, vol. 348(1), pages 317-343, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05401-7
    DOI: 10.1007/s10479-023-05401-7
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