Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures

In this paper selected energy commodities spot prices are forecasted with the help of Bayesian dynamic finite mixtures. In particular, crude oil, natural gas, and coal spot prices are analyzed. Due to the availability of data, crude oil is analyzed between 1988 and 2019, natural gas between 1990 and 2019, and coal between 1987 and 2019. Monthly data are used. The dynamic mixtures used herein are a novel methodological tool in forecasting. Their first important feature is that regression coefficients are estimated in a recursive on-line way, allowing for real-time performance. Secondly, the switching between mixture components is also allowed to vary in time. Thirdly, the algorithms used herein are based on explicit solutions, allowing for the fully Bayesian inference approach, whereas approximations are only on the numerical level of the pdfs (probability density functions) statistics. In other words, the evolution of prior to posterior pdfs has fixed functional form; only the numerical statistics of those pdfs are evolving in time. Both normal regression components and state-space models are considered as mixture components, which makes this study a generalization of previous research with Bayesian approaches to model averaging techniques. Indeed, those mixtures are compared with other benchmark models, such as Dynamic Model Averaging, Time-Varying Parameter regression, ARIMA, and the naïve method, with the Diebold-Mariano test, and are found to generate significantly more accurate forecasts. Additionally, the Giacomini-Rossi fluctuation test and Model Confidence Set are applied for more thorough examination of forecasting performances.