This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas.
Supplementary information: The online version contains supplementary material available at 10.1007/s00500-022-07276-5.
Keywords: Decision support; Machine learning; Mathematical finance; Support vector machine; Volatility forecasting.
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.