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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Improved inference in financial factor models
Organization Unit
  • Elliot Beck
  • Gianluca De Nard
  • Michael Wolf
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title International Review of Economics and Finance
Publisher Elsevier
Geographical Reach international
ISSN 1059-0560
Volume 86
Page Range 364 - 379
Date 2023
Abstract Text Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama–French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil.
Related URLs
Digital Object Identifier 10.1016/j.iref.2023.03.009
Other Identification Number merlin-id:23575
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Additional Information Bereits in der Working Paper Series / Department of Economics als No. 430 erschienen ( ).