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Research Details

Large Sample Estimators of the Stochastic Discount Factor

Abstract

We propose estimators of the stochastic discount factor (SDF) using large cross-sections of individual stocks. Our small-sample bias corrections allow us to exploit unbalanced panels of individual stock returns. Our estimators can accommodate prespecified traded and non-traded factors, or latent factors. The estimators perform well in simulations. We apply our estimators to return data for 10,112 individual stocks over a 50-year sample period, and identify those factors in popular asset pricing models that command significant premia. Contrary to many studies, we find the market factor has a significant premium, as do profitability, value, and momentum factors.

Type

Working Paper

Author(s)

Robert Korajczyk, Soohun Kim

Date Published

2020

Citations

Korajczyk, Robert, and Soohun Kim. 2020. Large Sample Estimators of the Stochastic Discount Factor.

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