Comparing and evaluating Bayesian predictive distributions of asset returns

John Geweke () and Gianni Amisano
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John Geweke: Departments of Statistics and Economics, University of Iowa, 430 N. Clinton St., Iowa City, IA 52242-2020, USA., http://www.uiowa.edu/
Authors registered in the RePEc Author Service: John Geweke and John Geweke

No 969, Working Paper Series from European Central Bank

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. JEL Classification: C11, C53.

Keywords: Forecasting; GARCH; inverse probability transform; Markov mixture; predictive likelihood; S&P 500 returns; stochastic volatility. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
Date: Written
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