Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures

This study investigates whether China's crude oil futures (INE) and West Texas Intermediate (WTI) markets hold valuable information for estimating the realized volatility of seven Asian stock markets. This study has several notable findings. First, China's oil futures can trigger forecast accuracy for three equity indices (Nikkei 225, NSEI, and FT Straits Times), whereas WTI helps forecast the volatility of the two indices (KSE 100 and KOSPI). Second, comparing China's crude oil futures with WTI's crude oil futures, we find that the former could be an effective indicator for all seven Asian stock markets during a high-volatility period, while WTI information is helpful in forecasting the volatility of the KSE 100, NSEI, and FT Strait Times during the low-volatility period. Further, information of both oil futures is ineffective for the Hang Seng and SSEC equity indices. Our results are robust in several robustness checks, including alternative evaluation methods, recursive window approach, and alternative realized measures, even during the COVID-19 pandemic.

Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?

In this paper, we try to forecast the volatility of Chinese crude oil futures (COF) using multiple economic policy uncertainty indicators. MIDAS-RV model is combined with the principal component analysis (PCA), scaled PCA (SPCA) and partial least squares (PLS) techniques in this work, construct the newly MIDAS-RV-PCA, MIDAS-RV-PLS and MIDAS-RV-SPCA models, their prediction performance is compared with the common combination forecasting methods. The in-sample estimation analysis on MIDAS-RV-X models show the that it is necessary to consider multiple economic policy uncertainty indices when predicting the Chinese COF volatility and the in-sample analysis on dimensionality reduction model further demonstrate the rationality of using dimensionality reduction techniques. The out-of-sample evaluation results show that the MIDAS-RV-SPCA is a superior model when forecasting the short-term volatility of Chinese COF using multiple economic policy uncertainty indicators, especially during the periods of high volatility and COVID-19 pandemic. The results also indicates that the DMSPE(0.9) method in the combination forecasting method shows its superior forecasting ability in long-term volatility of Chinese COF, especially during the low volatility and pre-pandemic period.