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

Davide Raggi and Silvano Bordignon

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3730-3742

Abstract: 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.

Keywords: Realized volatility; Switching-regime; Long memory; MCMC; Forecasting (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (48)

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Working Paper: Long memory and nonlinearities in realized volatility: a Markov switching approach (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3730-3742

DOI: 10.1016/j.csda.2010.12.008

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