In this study, we use an extension of the heterogeneous autoregressive model to investigate the influence of time-varying risk aversion and macroeconomic, financial, and economic policy uncertainty measures on stock market volatility and correlation. Based on the findings, there is a stronger predictive ability of these variables at the monthly frequency than at the daily frequency. We also highlight the importance of risk aversion, which, alongside fundamental factors, reflects investor sentiment in predicting stock market volatility. Meanwhile, although uncertainty variables, such as economic uncertainty and financial uncertainty, are important, the widely used variable, economic policy uncertainty, is not helpful for predicting stock market volatility. Moreover, there is evidence of higher economic value and reduced portfolio risk when including risk aversion and economic uncertainty in international portfolio analysis.