Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section

Daniele Bianchi, Massimo Guidolin and Francesco Ravazzolo

No 550, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

Abstract: This paper proposes a Bayesian estimation framework for a typical multi-factor model with timevarying 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 cross-section. A Bayes factor analysis massively favors of the proposed change-point model. Keywords: Structural breaks, Stochastic volatility, Multi-factor linear models, Asset Pricing. JEL codes: G11, E44, C11, C53

Date: 2015
New Economics Papers: this item is included in nep-ore and nep-rmg
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Related works:
Journal Article: Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section (2017)
Working Paper: Macroeconomic factors strike back: A Bayesian change-point model of time-varying risk exposures and premia in the U.S. cross-section (2013)
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