A Bayesian analysis of a variance decomposition for stock returns

B. Hollifield, G.M. Koop, K. Li

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


We apply Bayesian methods to study a common vector autoregression (VAR)-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy, which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including ''non-informative'' limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the ''non-informative'' prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.
Original languageEnglish
Pages (from-to)583-601
Number of pages18
JournalJournal of Empirical Finance
Issue number5
Publication statusPublished - Dec 2003


  • vector autoregression
  • priors
  • nonlinear functions
  • statistics
  • stock
  • shares
  • econometrics


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