Simulation-based sequential analysis of Markov switching stochastic volatility models
Computational Statistics & Data Analysis
Volatility forecasting using threshold heteroskedastic models of the intra-day range
Computational Statistics & Data Analysis
Real time detection of structural breaks in GARCH models
Computational Statistics & Data Analysis
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
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.