Do Extreme Range Estimators Improve Realized Volatility Forecasts? Evidence from G7 Stock Markets

We investigate whether range-based estimators contain information in forecasting realized volatility within a HAR-RV-X framework and applied to G7 stock markets. Using a rolling window approach and QLIKE, HMSE and MCS forecast criteria, overall findings suggest that while no single model dominates, overnight return volatility achieves the most consistency. For example, forecasts for CAC and DAX indices are improved only by overnight volatility, with some evidence also for SPX. For other indices, forecasts are improved by Parkinson and/or Garman-Klass volatility estimators. Notably, simpler range estimators outperform more complex ones. The findings are of importance for investors in managing portfolio risk