 Alternative econometric implementations of multi-factor models of the U.S. financial marketsMassimo Guidolin, Francesco Ravazzolo and Andrea Donato Tortora The Quarterly Review of Economics and Finance, 2013, vol. 53, issue 2, 87-111 Abstract: This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach introduced by Fama and MacBeth (1973). The second approach is based on a Bayesian latent mixture model with breaks in risk exposures and idiosyncratic volatility. Our application to monthly, 1980–2010 U.S. data on stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window estimation of time-varying betas yields results that are unreasonable. There is evidence that most portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results and a few factor risk premia are precisely estimated with a plausible sign. Predictive log-likelihood scores indicate that discrete breaks in both risk exposures and variances are required to fit the data. Keywords: Bayesian estimation; Latent jumps; Stochastic volatility; Linear factor models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:53:y:2013:i:2:p:87-111 DOI: 10.1016/j.qref.2013.01.004 Access Statistics for this article The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty More articles in The Quarterly Review of Economics and Finance from Elsevier |
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