Forecasting crude oil volatility with uncertainty indicators: New evidence

This paper uses multiple uncertainty indicators to forecast monthly WTI crude oil volatility and compare the predictive performance of combination forecast methods, dimension reduction techniques and two least absolute shrinkage and selection operator augmented MIDAS (MIDAS-LASSO and MS-MIDAS-LASSO) models. Some noteworthy findings are observed by using the MIDAS-RV extensions. First, among all uncertainty indicators, the U.S. petroleum market equity market volatility tracker index (PMEMV) statistically has the best short-term predictive power for the volatility of crude oil market, especially during low volatility, non-crisis and economic expansion periods. However, the geopolitical risk index (GPR) performs better in predicting long-term crude oil volatility than other uncertainty indicators, and it also performs better than other uncertainty indicators in forecasting short-term high volatility of crude oil market. In addition, the financial stress index (FSI) has better predictive ability than other uncertainty indicators during periods of crisis and economic recession. Finally, the newly constructed MS-MIDAS-LASSO and MIDAS-LASSO models always have much higher forecasting accuracy than combination forecast methods, dimension reduction techniques as well as the best MIDASRV-X models, with MS-MIDAS-LASSO model having greater forecasting accuracy than the MIDAS-LASSO model in most cases. This empirical finding is confirmed by a variety of robustness checks.

Automated summary

This paper forecasts the volatility of the crude oil market using multiple uncertainty indicators and compares the predictive performance of various forecast methods and models. The findings suggest that different uncertainty indicators have different predictive power in different situations, and the newly constructed models have higher accuracy than traditional methods.