Empirical Asset Pricing With Many Test Assets
55 Pages Posted: 28 Nov 2018 Last revised: 29 Dec 2023
Date Written: October 31, 2018
Abstract
We reformulate the problem of estimating risk prices in a stochastic discount factor model as an instrumental variables regression. The IV estimator allows efficient estimation for models with non-traded factors and many test assets. Optimal instruments are constructed using a regularised sparse first stage regression. In a simulation study the IV estimator is close to the infeasible GMM estimator in a setting with many assets. In an empirical application, the tracking portfolio for consumption growth appears strongly correlated with consumption news. It implies that consumption is a priced factor for the cross-section of excess equity returns. A similar regularised regression, projecting the stochastic discount factor on test assets, leads to an estimate of the Hansen-Jagannathan distance, and identifies portfolios that maximally violate the pricing implications of the model.
Keywords: Boosting, Asset Pricing Tests, Hansen-Jagannathan Distance
JEL Classification: G12, C44, C55
Suggested Citation: Suggested Citation