Portfolio Performance of Linear SDF Models: An Out-of-Sample Assessment
Massimo Guidolin (),
Erwin Hansen () and
No 627, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University
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.
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Journal Article: Portfolio performance of linear SDF models: an out-of-sample assessment (2018)
Working Paper: Portfolio Performance of Linear SDF Models: An Out-of-Sample Assessment (2018)
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