Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk

We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical risks for forecasting the future realized volatility of oil-price (WTI) returns over the monthly period from 1985:01 to 2021:08. Using machine-learning techniques, we find that adding the disaggregated metrics to the array of predictors improves the accuracy of forecasts at intermediate and long forecast horizons, and mainly when we use random forests to estimate our forecasting model.

Automated summary

This study contributes to the empirical literature by comparing the predictive role of aggregate and disaggregated metrics of policy-related and equity-market uncertainties, as well as geopolitical risks, for forecasting oil price volatility. Using machine-learning techniques, the study finds that adding disaggregated metrics improves the accuracy of forecasts, particularly at intermediate and long forecast horizons.