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GMM weighting matrices in cross-sectional asset pricing tests

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  • Laurinaityte, Nora
  • Meinerding, Christoph
  • Schlag, Christian
  • Thimme, Julian

Abstract

When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.

Suggested Citation

  • Laurinaityte, Nora & Meinerding, Christoph & Schlag, Christian & Thimme, Julian, 2024. "GMM weighting matrices in cross-sectional asset pricing tests," Journal of Banking & Finance, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:jbfina:v:162:y:2024:i:c:s0378426624000438
    DOI: 10.1016/j.jbankfin.2024.107123
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    More about this item

    Keywords

    Asset pricing; Cross-section of expected returns;

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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