A regime switching long memory model to forecast realized volatility

Goal of this paper is to analyze models to forecast the realized volatility. In particular, we propose a dynamics that simultaneously captures long memory and nonlinearities in which level and persistence shifts through a Markov switching approach. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent processes involved. Also Bayesian predictive densities have been obtained within such algorithm. The in-sample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results, obtained using several forecast horizons, show that introducing these nonlinearities produces superior long range forecasts over those obtained from nested models.