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.
All Science Journal Classification (ASJC) codes
- Economics, Econometrics and Finance(all)
Lozano Banda, M. C., Hansen, E., & Guidolin, M. (2018). Portfolio performance of linear SDF models: an out-of-sample assessment. Quantitative Finance, 18(8), 1425-1436. https://doi.org/10.1080/14697688.2018.1429646, https://doi.org/10.1080/14697688.2018.1429646