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Long memory and nonlinearities in realized volatility: a Markov switching approach. (English) Zbl 1255.62321

Summary: 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.

MSC:

62P05 Applications of statistics to actuarial sciences and financial mathematics
65C40 Numerical analysis or methods applied to Markov chains

Software:

SsfPack; ARFIMA; Ox
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Full Text: DOI Link

References:

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