GMM Weighting Matrices in Cross-Sectional Asset Pricing Tests

57 Pages Posted: 21 Nov 2017 Last revised: 15 Jul 2020

See all articles by Nora Laurinaityte

Nora Laurinaityte

Goethe University Frankfurt, House of Finance (HoF), Graduate School of Economics, Finance and Management (GSEFM) ; Leibniz Institute for Financial Research SAFE

Christoph Meinerding

Deutsche Bundesbank

Christian Schlag

Leibniz Institute for Financial Research SAFE

Julian Thimme

Karlsruhe Institute of Technology

Date Written: July 14, 2020

Abstract

Cross-sectional asset pricing tests with GMM can generate spuriously high explanatory power for factor models when the moment conditions are specified such that they allow the estimated factor means to substantially deviate from the observed sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. This property is a feature of the GMM estimation design and applies to strong as well as weak factors, and to all sample sizes and test assets. We reveal the origins of this bias theoretically, gauge its size using simulations, and document its relevance empirically.

Keywords: Asset pricing, cross-section of expected returns, GMM, factor zoo

JEL Classification: G00, G12, C21, C13

Suggested Citation

Laurinaityte, Nora and Meinerding, Christoph and Schlag, Christian and Thimme, Julian, GMM Weighting Matrices in Cross-Sectional Asset Pricing Tests (July 14, 2020). Available at SSRN: https://ssrn.com/abstract=3073197 or http://dx.doi.org/10.2139/ssrn.3073197

Nora Laurinaityte

Goethe University Frankfurt, House of Finance (HoF), Graduate School of Economics, Finance and Management (GSEFM) ( email )

Grüneburgplatz 1
Frankfurt
Germany

Leibniz Institute for Financial Research SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Christoph Meinerding (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

Christian Schlag

Leibniz Institute for Financial Research SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany
+49 69 798 33699 (Phone)

Julian Thimme

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

HOME PAGE: http://julianthimme.de

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