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Efficient Bayesian estimation and combination of GARCH type models

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Figure

Figure 1:The AdMit algorithm automatically and iteratively approximates the skewed shapes of the marginalposterior of ν in the EGARCH model.
Table 1:Posterior results for the two non-nested models using AdMitIS. numerical standard error of the posterior mean estimate
Figure 2: Marginal posterior densities for parameters in GJR model.
Figure 4: Predictive density of σT +1 for EGARCH model, GJR model and Bayesian model averaging (BMA) of thetwo models.

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