Portfolio Performance of Linear SDF Models: An Out-of-Sample Assessment
Massimo Guidolin (),
Erwin Hansen () and
No 1885, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy
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 find 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 differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.
JEL-codes: G11 G12 (search for similar items in EconPapers)
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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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