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Title: Weak factors robust Hansen-Jagannathan distance test Authors:  Lingwei Kong - University of Groningen (Netherlands) [presenting]
Abstract: The Hansen-Jagannathan (HJ) statistic is one of the most dominant measures of model misspecification in asset pricing models. However, the conventional HJ specification test procedure has a poor finite sample performance, and it can be size distorted even in large samples when factors exhibit small correlations with asset returns. Applied researchers are likely to over-reject a model when it is correctly specified. We provide a novel model specification test, which is robust against the presence of weak factors and more powerful than the HJ test, and we also offer a novel robust risk premia estimator. The empirical application documents the non-reliability of the traditional HJ test since it may produce counter-intuitive results, when comparing nested models, by rejecting a four-factor model but not its embedded reduced three-factor model. At the same time, the proposed method is practically more appealing and show support for a four-factor model for Fama-French portfolios.