Improving volatility forecasts: Evidence from range-based models

Abstract

Volatility models based on the daily high-low range have become increasingly popular. The high and low prices are easily available, yet the range contains very useful information about volatility. It has been established in the literature that range-based volatility models outperform standard volatility models based on closing prices. However, little is known about which range-based model performs the best. We therefore evaluate two range-based volatility models, i.e. CARR and Range-GARCH with the standard GARCH model and two asymmetric GARCH models, i.e., GJR and EGARCH, based on the Monte Carlo experiments and a wide sample of currencies and stock indices. For simulated time series, the range-based models outperform the standard GARCH model and asymmetric models, and the performance of the Range-GARCH model and the CARR model is similar. However, for real financial time series (six currency pairs and nine stock indices) the Range-GARCH model outperforms the standard GARCH, GJR, EGARCH, and CARR models, while ranking of the competing models is ambiguous. We argue that Range-GARCH is the best from the competing models.