Comparing and evaluating Bayesian predictive distributions of assets returns

Gianni Amisano 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)
Date: 2008-11
Note: 337895
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp969.pdf (application/pdf)

Related works:
Journal Article: Comparing and evaluating Bayesian predictive distributions of asset returns (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:2008969

Access Statistics for this paper

More papers in Working Paper Series from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().