Realized volatility is studied using nonlinear and highly persistent dynamics. In particular, a model is proposed that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. Inference is based on an efficient Markov chain Monte Carlo (MCMC) algorithm that is used to estimate parameters, latent process and predictive densities. The in-sample 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 using nested models.
RAGGI, DAVIDE (Corresponding)
|Data di pubblicazione:||2012|
|Titolo:||Long memory and nonlinearities in realized volatility: A Markov switching approach|
|Rivista:||COMPUTATIONAL STATISTICS & DATA ANALYSIS|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.csda.2010.12.008|
|Appare nelle tipologie:||2.1 Articolo su rivista |