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Portfolio performance of linear SDF models: an out-of-sample assessment

Massimo Guidolin, Erwin Hansen and Martín Lozano-Banda

Quantitative Finance, 2018, vol. 18, issue 8, 1425-1436

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 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 significant 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.

Date: 2018
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Working Paper: 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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DOI: 10.1080/14697688.2018.1429646

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