Bayesian estimation of the Gaussian mixture GARCH model
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Bayesian estimation of the Gaussian mixture GARCH modelDate
2007Citation
Computational Statistics & Data Analysis, 2007, 51, p. 2636 – 2652
Abstract
Bayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model where the
innovations are assumed to follow a mixture of two Gaussian distributions is performed. The mixture 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. Bayesian prediction of the
Value at Risk is also addressed providing point estimates and predictive intervals. The method is illustrated using the Swiss Market
Index.
Keywords
Bayesian inference
GARCH models
Griddy–Gibbs sampler
Mixtures models
Value at risk
GARCH models
Griddy–Gibbs sampler
Mixtures models
Value at risk
Editor version
ISSN
0167-9473