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.
A regime switching long memory model to forecast realized volatility / S. Bordignon; D. Raggi;. - STAMPA. - (2009), pp. 85-90. (Intervento presentato al convegno S.Co. 2009 Sixth Conference tenutosi a Politecnico di Milano nel 14-16 Settembre 2009).
A regime switching long memory model to forecast realized volatility
BORDIGNON, SILVANO;RAGGI, DAVIDE
2009
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.