Bayesian estimation of the Gaussian mixture GARCH model

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.

• Type: Journal
• Year: 2007
• Where: CSDA
• Authors: María Concepción Ausín, Pedro Galeano
• Keywords: CSDA 2007, Financial Time Series, GARCH Model, Griddy-Gibbs Sampler Implementation