Forecasting carbon market volatility with big data

Bangzhu Zhu (), Chunzhuo Wan, Ping Wang () and Julien Chevallier
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Bangzhu Zhu: Guangxi University
Chunzhuo Wan: Guilin University of Electronic Technology
Ping Wang: Jinan University
Julien Chevallier: 4IPAG Lab, IPAG Business School

Annals of Operations Research, 2025, vol. 348, issue 1, No 14, 317-343

Abstract: 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.

Keywords: Carbon market; Volatility forecasting; Wavelet analysis; SMOTETomek; Extreme gradient boosting; Genetic algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05401-7

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