Ying-Nan Cong

Deep learning approaches for high-frequency carbon futures price forecasting: A comparative analysis of recurrent, attention-based, convolutional, and hybrid models

This study examines the forecasting performance of standalone and hybrid deep learning models for EU allowance (EUA) futures prices using high-frequency open, high, low and close (OHLC) data and shows that recurrent neural network-based models generally outperform alternative architectures.