Forecasting realised volatility using regime-switching models

Abstract

This paper extends standard AR and HAR models for realised volatility (RV) forecasting to include nonlinearity through two broad regime-switching approaches, the smooth transition and Markov-switching methods. Using daily data for eight international stock markets over the period 2007–2021, a comprehensive comparison is provided using a range of forecast tests that includes statistical and economic (risk management) based metrics. The results show that regime-switching models provide a better in-sample fit and out-of-sample forecasting, although this latter result is less clear-cut at the daily horizon. In comparing the two nonlinear approaches, we find that the abrupt transition technique of the Markov-switching model is preferred to the smooth transition one. It is believed that our results will be of interest to those especially engaged in risk management practice as well as for those modelling market behaviour.