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

The Wharton School Research Paper

Jacobs Levy Equity Management Center for Quantitative Financial Research Paper

60 Pages Posted: 19 Sep 2013 Last revised: 2 Apr 2021

See all articles by Hui Chen

Hui Chen

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

Winston Dou

The Wharton School, University of Pennsylvania

Leonid Kogan

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

Date Written: April 1, 2021

Abstract

We formalize the concept of ``dark matter'' in asset pricing models by quantifying additional information the econometrician can obtain about the fundamental dynamics from asset pricing cross-equation restrictions. The dark matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark matter measure signifies a model's lack of internal refutability (weak power of 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. We illustrate its applications via (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

Chen, Hui and Dou, Winston and Kogan, Leonid, Measuring 'Dark Matter' in Asset Pricing Models (April 1, 2021). 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, The Wharton School Research Paper, Jacobs Levy Equity Management Center for Quantitative Financial Research Paper, Available at SSRN: https://ssrn.com/abstract=2326753 or http://dx.doi.org/10.2139/ssrn.2326753

Hui Chen

Massachusetts Institute of Technology ( email )

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617-324-3896 (Phone)

National Bureau of Economic Research (NBER) ( email )

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Winston Dou

The Wharton School, University of Pennsylvania ( email )

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Leonid Kogan (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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United States
617-253-2289 (Phone)
617-258-6855 (Fax)

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

National Bureau of Economic Research (NBER)

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