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Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns, CQER Working Paper 09-04

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Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns, CQER Working Paper 09-04

DATE: October 26, 2009

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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 1972 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.

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Federal Reserve Bank of Atlanta. "Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns, CQER Working Paper 09-04" in Center for Quantitative Economic Research Working Papers (October 26, 2009). https://fraser.stlouisfed.org/title/7165/item/656235, accessed on December 2, 2023.

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NOTE: JEL classification: C11, C53

Key words: forecasting, GARCH, inverse probability transform, Markov-mixture, predictive likelihood, S&P 500 returns, stochastic volatility

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