Measuring “Dark Matter” in Asset Pricing Models

Hui Chen (), Winston Dou and Leonid Kogan

No 26418, NBER Working Papers from National Bureau of Economic Research, Inc

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 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.

JEL-codes: C52 D81 E32 G12 (search for similar items in EconPapers)
Date: 2019-11
New Economics Papers: this item is included in nep-ecm, nep-mac, nep-ore and nep-rmg
Note: AP
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Citations: View citations in EconPapers (11)

Published as 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.

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Journal Article: Measuring “Dark Matter” in Asset Pricing Models (2024)
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