A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likeliho...

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Veröffentlicht in:Journal of empirical finance 2014-12, Vol.29, p.207-229
Hauptverfasser: Bauwens, Luc, De Backer, Bruno, Dufays, Arnaud
Format: Artikel
Sprache:eng
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Zusammenfassung:We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.
ISSN:0927-5398
1879-1727
DOI:10.1016/j.jempfin.2014.06.008