Bayesian estimation of the gaussian mixture garch model

Pedro Galeano

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: 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.

Date: 2005-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
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Citations: View citations in EconPapers (3)

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Journal Article: Bayesian estimation of the Gaussian mixture GARCH model (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws053605

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