Forecasting the aggregate oil price volatility in a data-rich environment

Abstract

This paper explores the effectiveness of a large set of indicators in forecasting crude oil price volatility, including uncertainty and market sentiment, macroeconomic indicators, and technical indicators. Using the OLS, LASSO regression, and various combination forecasts, we obtain several noteworthy findings. First, we determine which indicators most effectively forecast oil price volatility. Specifically, the uncertainty index is notable. Second, in general, combination strategies and LASSO produce statistically and economically significant forecasts. Third, the combined and LASSO strategies perform considerably better during recessions than expansions. Overall, our study provides which indicators and strategies can improve forecasting accuracy in the oil market.