This paper investigates the predictive content of risk‐neutral skewness (RNSK) for the dynamics of commodity futures prices. A trading strategy that buys futures with positive RNSK and sells futures with negative RNSK generates a significant excess return. Unlike traditional commodity risk factors' signals, the positive return generated from the RNSK signal is more pronounced in the contango phase. After controlling traditional commodity risk factors, the RNSK signal exhibits a more stable and prolonged predictive ability. The directional‐learning hypothesis explains the RNSK impact when commodity futures show higher idiosyncratic risks and illiquidity (positive RNSK) and overpriced (negative RNSK).

We propose a new functional change point detection procedure, motivated by recent models for commodity futures term structure. We investigate our procedure's properties under the null hypothesis of no change and the alternative. Monte Carlo simulations reveal a reasonable power property in finite sample sizes although size distortion persists. An empirical analysis of oil futures markets identifies two change points near the 2008 financial crisis and 2020 crude oil negative territory. Regression models show that the price behaviour, in general, is exposed to the spot market index and exchange rate from 2007 to 2009. The main drivers of the price term structure are attributed to the trading activities of speculators and financial index innovations between 2017 and 2022.