Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration

Abstract

Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision-makers, as well as improve the carbon market system. Nevertheless, existing methods for combination forecasting typically use one or a few specific methods. Specifically, various forecasting methods would provide practical information for carbon price forecasting from different perspectives due to the varying model settings. This study combines various models for carbon price forecasting. ARIMA, NARNN, LSTM, and 11 other single forecasting models are employed in this study, including both traditional statistical forecasting models and artificial intelligence forecasting models, to fully exploit the information provided by each method and establish the optimal combination forecasting model based on Lasso regression. First, the carbon price index is predicted using 11 single prediction models. Furthermore, given the multicollinearity of the single prediction series, this study employs Lasso regression to reduce the dimension of the single prediction model, which is then used to construct the optimal combination prediction model. Finally, the proposed model is applied to SZA-2017 and SZA-2019 carbon price data in Shenzhen. The results demonstrate that the model developed in this study outperforms other benchmark prediction models in terms of prediction error and direction accuracy, showing the efficacy of the proposed method.

Keywords: Carbon price forecasting, Lasso regression, Optimal combined forecasting model