Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102389
Authors: 
Guidolin, Massimo
Ravazzolo, Francesco
Tortora, Andrea Donato
Year of Publication: 
2011
Series/Report no.: 
Manchester Business School Working Paper 619
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 advocated by Fama and MacBeth (1973). The second approach is based on a Bayesian approach to modelling the latent process followed by risk exposures and idiosynchratic volatility. Our application to monthly, 1979-2008 U.S. data for stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window modelling of time-varying betas yields results that are unreasonable. There is evidence that all the portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results as most portfolios do not appear to have been misspriced and a few risk premia are precisely estimated with a plausible sign. Real consumption growth risk turns out to be the only factor that is persistently priced throughout the sample.
Subjects: 
Bayesian estimation
Latent jumps
Stochastic volatility
Linear factor models
JEL: 
G11
C53
Document Type: 
Working Paper

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