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A Bayesian View of Temporary Components in
Asset Prices
Bjørn Eraker∗
This draft: February 2007
Abstract
This paper studies models in which the a stock price contains a random
walk and a stationary component, as in Fama & French (1988) and Poterba
and Summers (1988). We extend this model to allow for two latent factors
which generate short term and long term autocorrelations, respectively. To
facilitate econometric identification, we assume that these factors are common
across multiple asset returns, and we estimate the factor loadings. In an ap-
plication to size and book/market sorted portfolios, we find the short term
factor economically and statistically insignificant. Estimates of parameters re-
lating to the long range component suggest that portfolios of small firm stock
display about three times the amount of mean reversion than for large firm
stocks. Overall, the evidence suggests that mean reversion is largely a small
firm phenomenon. The evidence is consistent with dynamic equilibrium models
in which asset prices co-integrate with aggregate consumption or dividends.
∗bjorn.eraker@duke.edu. I am grateful for comments from Michael Brandt, Tim Bollerslev,
George Tauchen and seminar participants at the Duke Financial Econometrics workshop. A pre-
vious version of the manuscript was circulated under the title ”Predicting Asset Returns using
Autoregressive Latent Component Models.”
1
1 Introduction
Few topics in finance have been debated as heavily as the random walk hypothesis of
secu
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