Carbon Price Forecasting with Quantile Regression and Feature Selection
This work addresses carbon price prediction for policy-making and industrial planning, but it appears incremental as it applies existing quantile regression techniques to a new financial asset.
The paper tackles carbon price forecasting by collecting various influencing factors, selecting significant ones for explainability, and using Sparse Quantile Group Lasso methods for robust predictions. The proposed methods outperform existing ones and provide a complete profile of future prices at different quantile levels.
Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market.