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Long memory and nonlinearities in realized volatility: A Markov switching approach

  • Raggi, Davide
  • Bordignon, Silvano

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.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3730-3742

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3730-3742
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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