Forecasting oil price realized volatility: A new approach

This paper adds to the extremely limited strand of the literature focusing on the oil price realized volatility forecasting. More specifically, we evaluate the information content of four different asset classes’ volatilities when forecasting the oil price realized volatility for 1-day until 66-day ahead. To do so, we concentrate on the Brent crude oil and fourteen other assets, which are grouped into four different asset classes, based on Heterogeneous AutoRegressive (HAR) framework. Our out-of-sample forecasting results can be summarised as follows. (i) The use of exogenous volatilities statistically significant improves the forecasting accuracy at all forecasting horizons. (ii) The HAR model that combines volatilities from multiple asset classes is the best performing model. (iii) The Direction of Change suggests that all HAR models are highly accurate in predicting future movements of oil price volatility. (iv) The forecasting accuracy of the models is better gauged using the Median Absolute Error and the Median Squared Error. (v) The findings are robust even during turbulent economic periods. Hence, different asset classes’ volatilities contain important information which can be used to improve the forecasting accuracy of oil price volatility.