Measuring 'Dark Matter' in Asset Pricing Models
NBER Working Paper Series
The Rodney L. White Center Working Papers Series at the Wharton School
The Jacobs Levy Equity Management Center for Quantitative Financial Research Working Papers Series
83 Pages Posted: 19 Sep 2013 Last revised: 30 Nov 2019
Date Written: November 26, 2019
Abstract
We introduce an information-based fragility measure for GMM models that are potentially misspecified and unstable. A large fragility measure signifies a GMM 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 the quantity of additional information the econometrician can obtain 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 via two models: 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
Suggested Citation: Suggested Citation
Register to save articles to
your library
