The paper by Tim Bollerslev and Hao Zhou explores the relationship between implied and realized variances, known as the variance risk premium, and its predictive power for stock market returns. The authors find that the variance risk premium explains more than 15% of the ex-post time series variation in quarterly excess returns on the market portfolio over the 1990 to 2005 period. High (low) premia predict high (low) future returns, and the predictive power of the variance risk premium is significantly greater than that of traditional predictor variables such as the P/E ratio, dividend yield, default spread, and the consumption-wealth ratio (CAY). Combining the variance risk premium with the P/E ratio results in an R² of over 25% for quarterly returns. The findings suggest that temporal variation in risk and risk aversion play crucial roles in determining stock market returns. The results are robust to using "model-free" implied and realized variances constructed from high-frequency intraday data, rather than standard Black-Scholes implied variances and daily realized variances. The authors interpret the variance risk premium as a measure of time-varying risk aversion, which influences consumption and investment decisions, leading to changes in expected excess returns and economic growth.The paper by Tim Bollerslev and Hao Zhou explores the relationship between implied and realized variances, known as the variance risk premium, and its predictive power for stock market returns. The authors find that the variance risk premium explains more than 15% of the ex-post time series variation in quarterly excess returns on the market portfolio over the 1990 to 2005 period. High (low) premia predict high (low) future returns, and the predictive power of the variance risk premium is significantly greater than that of traditional predictor variables such as the P/E ratio, dividend yield, default spread, and the consumption-wealth ratio (CAY). Combining the variance risk premium with the P/E ratio results in an R² of over 25% for quarterly returns. The findings suggest that temporal variation in risk and risk aversion play crucial roles in determining stock market returns. The results are robust to using "model-free" implied and realized variances constructed from high-frequency intraday data, rather than standard Black-Scholes implied variances and daily realized variances. The authors interpret the variance risk premium as a measure of time-varying risk aversion, which influences consumption and investment decisions, leading to changes in expected excess returns and economic growth.