It is commonly recognized that setting a reasonable carbon price can promote the healthy development of a carbon trading market, so it is especially important to improve the accuracy of carbon price forecasting. In this paper, we propose and evaluate a hybrid carbon price prediction model based on so-called double shrinkage methods, which combines factor screening, dimensionality reduction, and model prediction. In order to verify the effectiveness and superiority of the proposed model, this paper takes data from the Guangdong carbon trading market for empirical analysis. The sample interval is from 5 August 2013 to 25 March 2022. Based on the results of the empirical analysis, several main findings can be summarized. First, the double shrinkage methods proposed in this paper yield more accurate prediction results than various alternative models based on the direct application of factor screening methods or dimensionality reduction methods, when comparing R2, root-mean-square error (RMSE), and root absolute error (RAE). Second, LSTM-based double shrinkage methods have superior prediction performance compared to LR-based double shrinkage methods. Third, these findings are robust with the use of normalized data, different data frequencies, different carbon trading markets, and different dataset divisions. This study provides new ideas for carbon price prediction, which might have a theoretical and practical contributions to complex and non-linear time series analysis.
Keywords: carbon price forecasting; dimensionality reduction; double shrinkage methods; factor screening.