Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153403
Authors: 
Geweke, John
Amisano, Gianni
Year of Publication: 
2008
Series/Report no.: 
ECB Working Paper 969
Abstract: 
Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative models of asset returns applied to daily S&P 500 returns from 1976 through 2005. The comparison exercise uses predictive likelihoods and is inherently Bayesian. The evaluation exercise uses the probability integral transform and is inherently frequentist. The illustration shows that the two approaches can be complementary, each identifying strengths and weaknesses in models that are not evident using the other.
Subjects: 
forecasting
GARCH
inverse probability transform
Markov mixture
predictive likelihood
S&P 500 returns
stochastic volatility
JEL: 
C11
C53
Document Type: 
Working Paper

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