Bayesian estimation of the Gaussian mixture GARCH model
AbstractIn this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy-Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. The method is illustrated using the Swiss Market Index.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 51 (2007)
Issue (Month): 5 (February)
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Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- María Concepcion Ausin & Pedro Galeano, 2005. "Bayesian Estimation Of The Gaussian Mixture Garch Model," Statistics and Econometrics Working Papers ws053605, Universidad Carlos III, Departamento de Estadística y Econometría.
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