Measuring the 'Dark Matter' in Asset Pricing Models

Abstract

We introduce an information-based fragility measure for GMM models that are potentially misspecified and unstable. A high fragility measure reflects a model's lack of internal refutability (weak power of specification tests) and external validity (poor out-of-sample fit). The fragility of a set of model-implied moment restrictions is tightly linked to how much more the econometrician can learn about the model parameters by imposing these restrictions. Our fragility measure can be computed at little cost even for complex dynamic structural models. We illustrate its applications in a rare-disaster risk model and a long-run risk model.

Keywords: Fragile beliefs, Unstable models, Misspecification, Time series, Out-of-sample fit, Disaster risk, Long-run risk, Semiparametric efficiency

JEL Classification: C52, G12, D81, E32