Forecasting the Index of Commodities Prices Using Various Bayesian Models

Bayesian dynamic mixture models offer a flexible framework for capturing evolving relationships between dependent and independent variables over time. They address both structural and variable uncertainty, incorporating real-time market information through dynamic updating. Unlike static approaches, they allow the underlying process to change, which is particularly relevant for the fluctuating nature of commodity markets. In scenarios with a large number of possible predictors, various regression models can be employed, each yielding its own probability distribution for the coefficients. Forecasts are then constructed by combining these distributions using time-varying weights. This paper utilizes Bayesian dynamic mixture models to allow both the regression parameters and their associated weights to change over time. Computational efficiency is maintained by preserving distributional forms and limiting numerical approximations to statistics distributions. The study uses monthly Global Price Index of All Commodities from the International Monetary Fund, spanning the period 2003?2024. Key explanatory variables include interest rates, exchange rates, and stock market indices. The forecasting performance of the proposed models is compared to other techniques such as Dynamic Model Averaging, LASSO, ridge regression, and ARIMA, etc. Evaluation is conducted using the Diebold-Mariano test, Giacomini-Rossi test, Model Confidence Set procedure, and Clark-West test. (This research was funded in whole by National Science Centre, Poland, grant number 2022/45/B/HS4/00510.)