Google Index-Driven Oil Price Value-at-Risk Forecasting: A Decomposition Ensemble Approach

Abstract

The oil price is influenced not only by the fundamentals of supply and demand but also by unpredictable political conflicts, climate emergencies, and investor intentions, which cause enormous shortterm fluctuations in the oil price. The proposition of the Google index-driven decomposition ensemble model to forecast crude oil price risk uses big data technology and a time series decomposition method. First, by constructing an index of investor attention for the market and emergencies combined with a bivariate empirical mode decomposition, we analyze the impact of investor attention on oil price fluctuations. Second, we establish a vector autoregression model, and the impulse responses define the impact of emergencies on the crude oil price. Finally, with the help of machine learning and historical simulation methods, the risk of crude oil price shocks from unexpected events is predicted. Empirical research demonstrates that concerns related to the oil market and emergencies that appear in Google search data are closely related to changes in oil prices. Based on the Google index, our model's prediction of crude oil prices is more accurate than other models, and the prediction of value-at-risk is closer to the theoretical value than the historical simulation with the ARMA forecasts method. Considering the impact of emergencies in the prediction of crude oil price risk can help provide technical guidance for investors and risk managers and avoid economic risks caused by climate disasters or political conflicts.