Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model

Abstract

Many macro-level variables have been used in forecasting crude oil price volatility. This article aims to identify which variables have the greatest impact and give more accurate predictions. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model. By combining the log-likelihood function with adaptive lasso penalty, three most informative determinants have been identified, namely, macroeconomic uncertainty, financial uncertainty and default yield spread. Out-of-sample results show that using these three variables significantly improves prediction accuracy compared to baseline models. However, the variables widely studied by other scholars, such as the supply and demand of crude oil, industrial production index, etc., were not selected, indicating that the impact of these variables may be overestimated. When studying crude oil price volatility, macroeconomic and financial market uncertainties can be used as effective predictors for investors and market analysts. Crude oil market participants should focus on macroeconomic and financial market uncertainties to make risk management more efficient.