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
60 Pages Posted: 19 Sep 2013 Last revised: 25 Jan 2022
Date Written: January 25, 2022
We formalize the concept of ``dark matter'' in asset pricing models by quantifying the additional informativeness of cross-equation restrictions about fundamental dynamics. The dark matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark matter measure signifies that the model lacks internal refutability (weak power of optimal specification tests) and external validity (high overfitting tendency and poor out-of-sample fit). The measure can be computed at low cost even for complex dynamic structural models. To illustrate its applications, we provide quantitative examples applying the measure to (time-varying) rare-disaster risk and long-run risk models.
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