Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large-scale variables

This paper aims to study the volatility forecasting of Chinese crude oil futures from the large-scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO-PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large-scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO-PCA method. The empirical results show that both the OLS and SVR combined with LASSO-PCA can improve the forecasting accuracy, especially SVR-LASSO-PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out-of-sample forecasting.