Carbon Price Forecasting with Quantile Regression and Feature Selection
Tianqi Pang Kehui Tan Chenyou Fan
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.