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

Bordignon, Silvano ; Raggi, Davide (2010) Long memory and nonlinearities in realized volatility: a Markov switching approach. Bologna: Dipartimento di Scienze economiche DSE, p. 40. DOI 10.6092/unibo/amsacta/4547. In: Quaderni - Working Paper DSE (694). ISSN 2282-6483.
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Abstract

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample 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 from nested models.

Abstract
Document type
Monograph (Working Paper)
Creators
CreatorsAffiliationORCID
Bordignon, Silvano
Raggi, Davide
Keywords
Realized volatility, Switching-regime, Long memory, MCMC, Forecasting
Subjects
ISSN
2282-6483
DOI
Deposit date
04 Feb 2016 11:38
Last modified
04 Feb 2016 11:38
URI

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