This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID‐19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with t‐distributed innovations out performs the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the t‐distributed innovations remain the appropriate model for the COVID‐19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short‐term predictive ability—such as the conditional and the observed‐date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models.

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Bayesian econometrics,Commodities,Energy markets,Futures contracts,Markov chain Monte Carlo,Stochastic volatility

• Economics and Business - Social Sciences