Incorporating weather information into commodity portfolio optimization

This study investigates the out-of-sample performance of commodity portfolios by incorporating weather information within the Black-Litterman framework. The inclusion of weather information increases returns, reduces downside risk for energy and agricultural portfolios, and diminishes volatility in agricultural portfolios. We find significant enhancement in the efficiency of energy and agricultural portfolios with weather information. Notably, portfolios integrating low-temperature weather information outperform their counterparts across most performance measures. Our findings underscore the benefits of incorporating weather information in the optimization of commodity portfolios.