Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets

Citation

Korkusuz B, Kambouroudis D & McMillan DG (2023) Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets. Finance Research Letters. https://doi.org/10.1016/j.frl.2023.103992

Abstract
This paper investigates whether range estimators contain important information in forecasting future realized volatility. We use widely applied range-based estimators: Parkinson, Garman-Klass, Roger-Satchell, and Yang-Zhang within a HAR-RV-X framework. Overnight volatility and close-to-close volatility estimators are also included, and the forecasting exercise is applied to G7 stock markets using a rolling window. Using QLIKE, HMSE and MCS forecast criteria, several noteworthy points are reported. The overall findings suggest that while no single model dominates, overnight return volatility achieves the most consistent performance. For example, HAR-RV model 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. Of note, simpler range estimators outperform more complex range estimators. The findings could be important for investors in managing portfolio risk.

Keywords
Volatility forecasting; Realized volatility; G7 stock markets; HAR-RV-X model; Rolling methods; MCS

Notes
Output Status: Forthcoming/Available Online

Journal
Finance Research Letters

• Extreme Range Estimators And Realized Volatility Final