Measuring “Dark Matter” in Asset Pricing Models
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.
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Copy CitationHui Chen, Winston Wei Dou, and Leonid Kogan, "Measuring “Dark Matter” in Asset Pricing Models," NBER Working Paper 26418 (2019), https://doi.org/10.3386/w26418.
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Published Versions
HUI CHEN & WINSTON WEI DOU & LEONID KOGAN, 2024. "Measuring “Dark Matter” in Asset Pricing Models," The Journal of Finance, vol 79(2), pages 843-902.