Empirical Asset Pricing with Many Test Assets

Abstract We formulate the problem of estimating risk prices in a stochastic discount factor (SDF) 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 regulariz...

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Bibliographic Details
Published in:Journal of financial econometrics 2024-12, Vol.22 (5), p.1236-1263
Main Authors: Lönn, Rasmus, Schotman, Peter C
Format: Article
Language:English
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Summary:Abstract We formulate the problem of estimating risk prices in a stochastic discount factor (SDF) 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 regularized 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 regularized regression, projecting the SDF on test assets, leads to an estimate of the Hansen–Jagannathan distance, and identifies portfolios that maximally violate the pricing implications of the model.
ISSN:1479-8409
1479-8417
DOI:10.1093/jjfinec/nbae002