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Bayesian estimation of the gaussian mixture garch model

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URI: https://hdl.handle.net/10016/225
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ws053605.pdf (675.2 KB)
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2005-05
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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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DES - Working Papers. Statistics and Econometrics. WS