Measuring 'Dark Matter' in Asset Pricing Models

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 examples: a rare-disaster risk model and a long-run risk model.

Keywords: Fragile beliefs, Unstable models, Misspecification and robustness, Out-of-sample fit, Semiparametric information bounds

JEL Classification: C52, G12, D81, E32

Suggested Citation: Suggested Citation

Chen, Hui and Dou, Winston and Kogan, Leonid, Measuring 'Dark Matter' in Asset Pricing Models (December 31, 2019). 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; The Wharton School Research Paper. Available at SSRN: https://ssrn.com/abstract=2326753 or http://dx.doi.org/10.2139/ssrn.2326753