期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 56, 期 11, 页码 3730-3742出版社
ELSEVIER
DOI: 10.1016/j.csda.2010.12.008
关键词
Realized volatility; Switching-regime; Long memory; MCMC; Forecasting
资金
- MIUR [2008MRFM2H_003]
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. (C) 2010 Elsevier B.V. All rights reserved.
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