This paper proposes a Bayesian estimation framework for a typical
multi-factor model with time-varying risk exposures to macroeconomic
risk factors and corresponding premia to price U.S. publicly traded
assets. The model assumes that risk exposures and idiosynchratic
volatility follow a break-point latent process, allowing for changes at
any point on time but not restricting them to change at all points. The
empirical application to 40 years of U.S. data and 23 portfolios shows
that the approach yields sensible results compared to previous two-step
methods based on naive recursive estimation schemes, as well as a set
of alternative model restrictions. A variance decomposition test shows
that although most of the predictable variation comes from the market
risk premium, a number of additional macroeconomic risks, including
real output and inflation shocks, are significantly priced in the crosssection.
A Bayes factor analysis massively favors of the proposed
change-point model.