Measuring the 'Dark Matter' in Asset Pricing Models

83 Pages Posted: 19 Sep 2013 Last revised: 18 Sep 2019

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: September 9, 2019

Abstract

We introduce an information-based fragility measure for GMM models that are potentially misspecified and unstable. A high fragility measure reflects a model's lack of internal refutability (weak power of specification tests) and external validity (poor out-of-sample fit). The fragility of a set of model-implied moment restrictions is tightly linked to how much more the econometrician can learn about the model parameters by imposing these restrictions. Our fragility measure can be computed at little cost even for complex dynamic structural models. We illustrate its applications in a rare-disaster risk model and a long-run risk model.

Keywords: Fragile beliefs, Unstable models, Misspecification, Time series, Out-of-sample fit, Disaster risk, Long-run risk, Semiparametric efficiency

JEL Classification: C52, G12, D81, E32

Suggested Citation

Chen, Hui and Dou, Winston and Kogan, Leonid, Measuring the 'Dark Matter' in Asset Pricing Models (September 9, 2019). Available at SSRN: https://ssrn.com/abstract=2326753 or http://dx.doi.org/10.2139/ssrn.2326753

Hui Chen (Contact Author)

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

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

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Cambridge, MA 02142
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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Cambridge, MA 02138
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