Abstract:
Because the U.S. is a major player in the international oil market, it is interesting to
study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of oil price returns. To address this research question, we frame
our analysis in terms of variants of the popular heterogeneous autoregressive realized
volatility (HAR-RV) model. To estimate the models, we use quantile-regression and
quantile machine learning (Lasso) estimators. Our estimation results highlights the differential efects of economic conditions on the quantiles of the conditional distribution
of realized volatility. Using weekly data for the period April 1987 to December 2021, we
document evidence of predictability at a biweekly and monthly horizon.
