Integrating sentiment information for risk prediction: the case of crude oil futures market in China

This paper incorporates investor sentiment indexes into the traditional standard heterogeneous autoregressive (HAR) model to improve its power on predicting crude oil futures risk. Using the 5-min high-frequency trading data to construct the daily realized volatility, the original and revised HAR models are used for in-sample regression and out-of-sample forecasting on a daily, weekly, and monthly basis. The results show that the sentiment indexes and the search trend contain incremental information for forecasting the realized volatility of INE crude oil futures in the short and medium term. The search volume is the best indicator for weekly risk forecasting of INE crude oil futures. No robust index can improve the performance of HAR-type model on long-term risk prediction. This paper thus highlights that market participants should select appropriate strategies to minimize risk when volatility is at stake for their decisions.