Measuring “Dark Matter” in Asset Pricing Models

61 Pages Posted: 3 Dec 2019 Last revised: 9 Mar 2022

See all articles by Hui Chen

Hui Chen

Massachusetts Institute of Technology; National Bureau of Economic Research (NBER)

Winston Wei Dou

The Wharton School, University of Pennsylvania; National Bureau of Economic Research (NBER)

Leonid Kogan

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)

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Date Written: November 2019

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.

Suggested Citation

Chen, Hui and Dou, Winston Wei and Kogan, Leonid, Measuring “Dark Matter” in Asset Pricing Models (November 2019). NBER Working Paper No. w26418, Available at SSRN: https://ssrn.com/abstract=3496465

Hui Chen (Contact Author)

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Winston Wei Dou

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Leonid Kogan

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HOME PAGE: http://web.mit.edu/lkogan2/www/

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