A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting. (10th December 2018)

• Record Type: Journal Article
• Authors: Zhang, Jinliang; Li, Dezhi; Hao, Yu; Tan, Zhongfu
• Is Part Of: Journal of cleaner production. Volume 204(2018)
• Page Start: 958
• Page End: 964
• Publication Date: 2018-12-10
• Subjects: Carbon spot price forecasting -- Hybrid model -- Prediction precision -- EU ETS; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.09.071
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Carbon spot price forecasting result is important for both policymakers and market participants. However, because of the complex features of carbon spot price, accurate forecasting is very difficult. To achieve a better prediction precision, a hybrid model combined with complete ensemble empirical mode decomposition (CEEMD), co-integration model (CIM), generalized autoregressive conditional heteroskedasticity model (GARCH), and grey neural network (GNN) optimized by ant colony algorithm (ACA) is proposed. Then it is validated by using data collected from European Union emission trading scheme (EU ETS). The results indicate that the performance of the chosen model is remarkably better than that of other models. Therefore, the hybrid model could be used more frequently for carbon spot price forecasting in the future. Graphical abstract: