Exploring the predictability of attention mechanism with LSTM Evidence from EU carbon futures prices-张培刚发展研究院

This paper forecasts the price dynamics of carbon futures in the form of return under the EU emission trading scheme by using an attention mechanism based long short-term memory (AttLSTM) neural network. Prediction of the carbon price dynamics exploits not only historical information of itself but also that of its key predictors, including the price dynamics in fossil energy and stock markets. We find that the attention mechanism can significantly improve the LSTM prediction for the carbon price dynamics. The superior predictability of AttLSTM is examined by its lower MSE, MAE, and RMSE values in the out-of-sample forecasting against a standard LSTM prediction both in various parameter settings and tuning experiments, respectively. This is further demonstrated by the Wilcoxon signed rank test and Diebold Marian test. Our results reveal strong predictive performance of the AttLSTM for the carbon futures price dynamics, and corresponding implications should be of interest to various stakeholders.

ISSN:1878-3384

链接：Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices - ScienceDirect