Ausín Olivera, María ConcepciónGaleano, Pedro2006-11-092006-11-092005-05https://hdl.handle.net/10016/225In 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.691407 bytesapplication/pdfengworking paperEstadísticaopen accessws053605