Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of-sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.