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Portfolio Performance of Linear SDF Models: An Out-of-Sample Assessment

Massimo Guidolin (), Erwin Hansen () and Martín Lozano-Banda

No 627, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

Abstract: We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean-variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968-2016, we nd evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean-variance decisions implied by the single-factor CAPM, we document statistically signi cant di¤erences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample t also yield the highest realized Sharpe ratios. JEL classi cation: G11, G12. Keywords: Linear asset pricing models, Stochastic discount factor, Portfolio selection, Out-of-sample performance.

Date: 2018
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Journal Article: Portfolio performance of linear SDF models: an out-of-sample assessment (2018) Downloads
Working Paper: Portfolio Performance of Linear SDF Models: An Out-of-Sample Assessment (2018) Downloads
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