Modeling Volatility Dynamics in Emerging Markets: Novel Evidence From Large Set of Predictors

This study examines the effectiveness of different predictors to forecast volatility of E7 emerging markets. First, we employ the economic uncertainty factors information to find out the economic impact on stock market volatility. Second, we used the geopolitical uncertainty factor information to explore the influence of geopolitical events on stock market volatility. Third, we investigate the climate risk factors to forecast the stock market volatility. Fourth, we examine the energy market–related uncertainty factors' impact on equity market volatility (EMV). The out‐of‐sample results show that among all predictors, economic policy uncertainty (EPU), EMV, geopolitical risk (GPR), and the environmental social and governance (ESG) index have the better forecasting ability to predict stock market volatility. Additionally, we find evidence during different business conditions, recession and expansion, and the most recent COVID‐19 pandemic. The results are identical during recession periods and COVID‐19 pandemic. Notably, these indices proved their superior predictive performance for volatility estimation. Finally, to ensure the robustness of our findings, we use different forecasting window method. Considering a range of uncertainty factors allows for a more comprehensive understanding of market fluctuations. These results suggest that policymakers and investors should consider the volatility dynamics of uncertainty factors in financial decision‐making and policy realms.