This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR) modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 for gold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure.

Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.