Improved inference in financial factor models

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 430

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Hypothesis Testing: General ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Thema: CAPM ; conditional heteroskedasticity ; factor models ; HC standard errors

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Beck, Elliot ; De Nard, Gianluca ; Wolf, Michael
• Ereignis: Veröffentlichung
• (wer): University of Zurich, Department of Economics
• (wo): Zurich
• (wann): 2023

Weitere Informationen

• DOI: doi:10.5167/uzh-232341
• Handle: http://hdl.handle.net/10419/270437
• Letzte Aktualisierung: 20.09.2024, 08:22 MESZ