Bayesian Reconciliation of Return Predictability
Investigates return predictability using a Bayesian approach, comparing it to OLS and reduced-bias estimators.
What it examines
This paper investigates stock market return predictability using a Bayesian approach within a vector autoregressive (VAR) model, focusing on asset returns and the dividend-price ratio. It aims to address biases in parameter estimation and inference, comparing Bayesian methods to ordinary least squares and reduced-bias estimators.
What it concludes
The study concludes that Bayesian methods offer significant improvements in estimating return predictability, with potential applications in financial econometrics. Future research could explore further refinements in Bayesian priors and their applications to other financial models.
Evidence objects
The study concludes that Bayesian methods offer significant improvements in estimating return predictability, with potential applications in financial econometrics. Future research could explore further refinements in Bayesian priors and their applications to other financial models.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The VAR system comprises asset returns and the dividend-price ratio as proposed in Cochrane (2008), and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and c… ▽ More This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The VAR system comprises asset returns and the dividend-price ratio as proposed in Cochrane (2008), and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. △ Less
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