This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of...

This paper evaluates the predictability of WTI light sweet crude oil futures by using the variance risk premium, i.e. the difference between model-free measures of implied and realized volatilities. Additional regressors known for their ability to explain crude oil futures prices are also...

This study proposes a new VMD-ICEEMDAN-LSTM model, that combines secondary decomposition with long short-term memory neural networks (LSTM) to forecast realized volatility (RV) of Chinese crude oil futures. First, RV sequence is decomposed into sub-components and residuals using variational mode...