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: 11 Aug 2020

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: December 31, 2019

Abstract

We introduce an information-based fragility measure for GMM models that are potentially misspecified and unstable. A large fragility measure signifies a GMM 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 the quantity of additional information the econometrician can obtain 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 via two examples: a rare-disaster risk model and a long-run risk model.

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 (December 31, 2019). 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 )

50 Memorial Drive
Cambridge, MA 02142
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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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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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