Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model

In this study, we investigate the impacts of common information between oil futures and the United States stock markets on forecasting oil volatility using the multivariate heterogeneous autoregressive realized volatility model. We have several noteworthy findings. First, the in-sample estimation results show that the negative returns and the 'bad' jumps have larger influence on oil futures volatility than positive returns and 'good' jumps, respectively. Second, the out-of-sample results further indicate that our multivariate heterogeneous autoregressive (HAR) model can generate higher forecast accuracy than individual HAR model for the realized volatility-type models, which highlights the importance of incorporating volatility information from the stock Market. Third, our findings are robust across the direction-of-change test and different windows. Furthermore, from the perspective of the investors' allocation portfolio in the future, the use of common information can also bring greater gains.