We construct a robust stochastic discount factor (SDF) that summarizes the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on...

Performance evaluation of venture-capital (VC) payoffs is challenging because payoffs are infrequent, skewed, realized over endogenously varying time horizons, and cross- sectionally dependent. We show that standard stochastic discount factor (SDF) methods can be adapted to handle these issues....

When expected returns are linear in asset characteristics, the stochastic discount factor (SDF) that prices individual stocks can be represented as a factor model with GLS cross-sectional regression slope factors. Factors constructed heuristically by aggregating individual stocks into...

Performance evaluation of venture-capital (VC) payoffs is challenging because payoffs are infrequent, skewed, realized over endogenously varying time horizons, and cross- sectionally dependent. We show that standard stochastic discount factor (SDF) methods can be adapted to handle these issues....

We construct a robust stochastic discount factor (SDF) that summarizes the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on...