Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/270437 
Year of Publication: 
2023
Series/Report no.: 
Working Paper No. 430
Publisher: 
University of Zurich, Department of Economics, Zurich
Abstract: 
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.
Subjects: 
CAPM
conditional heteroskedasticity
factor models
HC standard errors
JEL: 
C12
C13
C21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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