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Bayesian Estimation Of The Gaussian Mixture Garch Model Author info | Abstract | Publisher info | Download info | Related research | Statistics María Concepcion Ausin ()
Pedro Galeano ()
In 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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Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number
ws053605.
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Date of creation: May 2005Date of revision:
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