An Interval-Based Transformer Approach for Carbon Future Prices Forecasting

We propose a novel interval-based transformer approach called DKformer to forecast quarterly carbon futures prices by treating interval-valued data as inseparable random sets. Our DKformer model leverages the attention mechanism to capture long-term dependencies and complex dynamics in the carbon market. The model integrates the European economic policy uncertainty (EEPU) index and the climate policy uncertainty (CPU) index, utilizing only lags of price values and EPU indices as explanatory variables to effectively capture the impact of policy shifts on carbon futures prices. Empirical tests demonstrate the strong performance of the model in predicting market turning points and responding to various policy change scenarios, including the implementation of the EU ETS Phase 4, the RePowerEU Plan, and the carbon border adjustment mechanism (CBAM). Furthermore, our findings indicate the outstanding performance of our method remains robust to various forecasting period, forecasting horizons, and sets of explanatory variables.