Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/159535
Authors: 
Bordignon, Silvano
Raggi, Davide
Year of Publication: 
2010
Series/Report no.: 
Quaderni - Working Paper DSE 694
Abstract: 
Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Working Paper

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