Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results

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This study contributes to the growing literature that focuses on predicting crude oil spot price returns out-of-sample by conditioning on the news-based uncertainty measures pioneered by Baker et al. (2016). With the aim of providing new empirical results useful for future research, we apply a comprehensive Bayesian model averaging (BMA) framework that incorporates the following aspects: (i): Parameter instability, (ii): Model uncertainty, and (iii): Besides the conditional mean process, it allows predictors of the candidate models in the model set to impact the variable being predicted through the conditional volatility process or both processes. Applied to monthly news-based uncertainty and crude oil price data from 1985m1 through 2020m12, we observe that accounting for model uncertainty and allowing predictors to impact crude oil price returns exclusively through the conditional volatility process lead to the most consistent pattern of point (density) prediction accuracy gains relative to the benchmark. In contrast, the approach predominately relied on in the current literature, namely, allowing predictors to impact returns only through the conditional mean process does not lead to the same degree of point (density) prediction accuracy gains. Likewise, any further prediction accuracy gains from (i) are at best modest once (ii) and (iii) are accounted for. The largest relative gain occurs when predicting the left tail of the conditional return distribution one-month ahead. The statistical evidence of predictability also translates to economic gains.