Forecasting the Chinese Stock Market Volatility with G7 Stock Market Volatilities: A Scaled PCA Approach

In this study, we forecast the realized volatility (RV) of the Chinese stock market using the heterogeneous autoregressive (HAR) model and various extended models. To extract the volatility information from the G7 stock markets, we employ a newly proposed approach, the scaled principal component analysis (SPCA), to produce a diffusion index and extend the HAR benchmark (HAR-SPCA). To validate the effectiveness of the SPCA approach, we employ three other dimension reduction approaches, the kitchen sink model, and five popular forecast combinations to deal with multivariate information and make competing forecasts. The results suggest that the combined volatility information from the G7 stock markets significantly predicts Chinese stock market volatility. More importantly, the forecasts from the HAR-SPCA model are steadily more accurate than the benchmark and other competing models under various evaluation criteria. Finally, our results are persistent to various robustness checks and the evaluation of portfolio performance.