Measuring “Dark Matter” in Asset Pricing Models

83 Pages Posted: 3 Dec 2019 Last revised: 11 Dec 2019

See all articles by Hui Chen

Hui Chen

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

Winston Wei Dou

University of Pennsylvania

Leonid Kogan

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

Date Written: November 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 models: a rare-disaster risk model and a long-run risk model.

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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)

Massachusetts Institute of Technology ( email )

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National Bureau of Economic Research (NBER) ( email )

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Winston Wei Dou

University of Pennsylvania ( email )

Philadelphia, PA 19104
United States

Leonid Kogan

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

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E62-636
Cambridge, MA 02142
United States
617-253-2289 (Phone)
617-258-6855 (Fax)

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

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