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We investigate the impact of the global economic policy uncertainty (GEPU) on stock volatility for nine emerging economies (Brazil, Russia, India, China, South Africa, Mexico, Indonesia, South Korea, and Turkey). We employ an expanded GARCH-MIDAS approach to connect low-frequency GEPU data and high-frequency stock data, assuming that GEPU affects stock volatility via the long-run component of total volatility. We not only use DM and SPA tests to statically evaluate the out-of-sample forecasting ability of the extended model, taking traditional GARCH model and GARCH-MIDAS model as benchmarks, but also use the Fluctuation test to examine the time-varyingly relative forecasting performance in the presence of potential instability. From the in-sample estimation results, we find that GEPU has empirically significant impact on stock volatility for the nine emerging economies. The out-of-sample forecasting results show that the GEPU-based model can improve forecasting performance of stock volatility for emerging markets, especially in unstable environments.

In this study, we constructed two pandemic anxiety indexes based on an assumption that people's emotions fluctuate with the COVID-19 reported cases and deaths, to examine the dynamic co-movements between these anxiety indexes and the stock markets in the BRICS and G7 countries. We found that the anxiety indexes are volatile over time but have an overall downtown trend. The correlations between stock market returns and the epidemic anxiety indexes are time varying. We found a common feature across the countries studied, namely that the correlation becomes weaker and has smaller fluctuations after the announcement of the mRNA-based COVID-19 vaccine.