A Bayesian multi-factor model of instability in prices and quantities of risk in U.S. financial markets

Massimo Guidolin, Francesco Ravazzolo and Andrea Donato Tortora

No 2011-003, Working Papers from Federal Reserve Bank of St. Louis

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 extends previous work by Ouysse and Kohn (2010) and 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 plausibile sign. Real consumption growth risk turns out to be the only factor that is persistently priced throughout the sample.

Keywords: Econometric models; Stochastic analysis; Financial markets (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-bec
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Citations: View citations in EconPapers (4)

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