Time-varying partial-directed coherence approach to forecast global energy prices with stochastic volatility model

For investors and policymakers, forecasting energy prices with accuracy is essential and plays a major role in the global bulk commodity markets. The current study proposes a novel hybrid forecasting model to predict global energy prices, namely, time-varying partial-directed coherence with stochastic volatility. The proposed method combines partial-directed coherence analysis and stochastic volatility models. Accordingly, this study attempts to provide an in-depth understanding of the relationship between energy markets and global economic conditions as well as the causality pathway between the underlined markets. Monthly data from January 1982 to July 2022 is used in this study. The results show a strong causality between global economic conditions, European oil, and natural gas prices and have profound implications for policymakers. For completeness, we extend the analysis to the forecasting ability of global economic conditions for oil and natural gas prices. The out-of-sample results show that the autoregressive model incorporating the global economic conditions index can significantly improve the accuracy of oil and gas price forecasts. In addition, our results are strongly robust over a variety of time horizons for forecasting, and they provide valuable insights into the forecasting choices to guide investment strategies in the energy and financial market.