Measuring “Dark Matter” in Asset Pricing Models

ABSTRACT 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 indicates 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.