Post by Sapphire Capital on Jan 16, 2009 22:36:54 GMT 4
Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns
John Geweke
University of Iowa - Department of Economics
Gianni Amisano
European Central Bank (ECB); University of Bologna - Rimini Center for Economic Analysis; University of Brescia - Department of Economics
November 26, 2008
ECB Working Paper No. 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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1299538_code485639.pdf?abstractid=1299538&mirid=1
John Geweke
University of Iowa - Department of Economics
Gianni Amisano
European Central Bank (ECB); University of Bologna - Rimini Center for Economic Analysis; University of Brescia - Department of Economics
November 26, 2008
ECB Working Paper No. 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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1299538_code485639.pdf?abstractid=1299538&mirid=1