The Predictive Content of Commodity Futures

This report examines the predictive content of commodity futures prices for energy, agricultural, precious, and base metals, assessing their unbiasedness and accuracy as predictors of future prices.

(Generated with the help of GPT-4)

The researchers employed statistical analysis, including Ordinary Least Squares (OLS) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, to examine the relationship between futures prices and subsequent spot prices across various commodities. They also used rolling regressions to assess time variation in predictive content.

(Generated with the help of GPT-4)

The research analyzes commodity futures prices to determine if they are unbiased and accurate predictors of future prices. It finds significant differences across commodity groups, with energy and agricultural futures performing better than metals. The study also notes a decline in the predictive content of commodity futures since the early 2000s and explores factors like market liquidity and time variation in futures properties.

(Generated with the help of GPT-4)

Categories: English publication language | Global geographic scope | agricultural | base metals | commodities | commodity futures | efficient markets hypothesis | energy | forecasting | futures | liquidity | market efficiency | precious metals | predictive content | prices | resource | time variation | unbiasedness