A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting

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.