A Regime-Switching Conditional Volatility Model Based on the Intraday Range

In this paper, we develop a component Markov switching conditional volatility model based on the intraday range and evaluate its performance in forecasting the weekly volatility of the S&P 500 index. We compare the performance of the range-based Markov switching model with that of a number of well-established return-based and range-based volatility models, namely the EWMA, GARCH and FIGARCH models, the Markov Regime-Switching GARCH model of Klaassen (2002), the hybrid EWMA model of Harris and Yilmaz (2010) and the CARR model of Chou (2005). We show that the range based Markov switching conditional volatility models produce more accurate out-of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for the estimation of value at risk