Crude Oil Markets Volatility Forecasting: A Novel Deep Learning Hybrid Model

Abstract

To the national economy, increasing the forecasting accuracy of realised volatility (RV) on crude oil futures markets is of critical strategic importance. However, the RV of crude oil futures cannot be accurately predicted with a single model. For this study, we adopt a hybrid model which combines gated recurrent unit (GRU) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the RV of crude oil futures. Moreover, back propagation neural networks (BP), Elman neural networks (Elman), support vector regression machine (SVR), autoregressive model (AR), heterogeneous autoregressive model (HAR), and their hybrid models with CEEMDAN are adopted as comparisons. In general, this article demonstrates the superiority of the CEEMDAN-GRU model in RV forecasting from several aspects: for both evaluation criteria, CEEMDAN-GRU achieves the highest RV forecasting accuracy in emerging and developed crude oil futures markets; furthermore, the empirical results are robust to alternative realised measures and training sets of different lengths.