A Bayesian analysis of a variance decomposition for stock returns

Koop, G., Hollifield, B. and Li, K. (2003) A Bayesian analysis of a variance decomposition for stock returns. Journal of Empirical Finance, 10(5), pp. 583-601. (doi: 10.1016/S0927-5398(03)00006-9)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: UNSPECIFIED
• Authors: Koop, G., Hollifield, B., and Li, K.
• College/School: College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Journal of Empirical Finance