Misspecification and Weak Identification in Asset Pricing

TL;DR

This paper addresses the issues of misspecification and weak identification in asset pricing models, proposing a robust testing method to reliably infer risk premia despite these challenges.

Contribution

It introduces a double robust Lagrange multiplier test that remains reliable under misspecification and weak identification, and clarifies how to interpret risk premia based on specific statistical measures.

Findings

Misspecification and weak identification are common in asset pricing models.

The proposed test provides more reliable risk premia estimates.

Empirical analysis shows widespread issues in existing factor models.

Abstract

The widespread co-existence of misspecification and weak identification in asset pricing has led to an overstated performance of risk factors. Because the conventional Fama and MacBeth (1973) methodology is jeopardized by misspecification and weak identification, we infer risk premia by using a double robust Lagrange multiplier test that remains reliable in the presence of these two empirically relevant issues. Moreover, we show how the identification, and the resulting appropriate interpretation, of the risk premia is governed by the relative magnitudes of the misspecification J-statistic and the identification IS-statistic. We revisit several prominent empirical applications and all specifications with one to six factors from the factor zoo of Feng, Giglio, and Xiu (2020) to emphasize the widespread occurrence of misspecification and weak identification.