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Improved inference in financial factor models

Elliot Beck, Gianluca De Nard and Michael Wolf

No 430, ECON - Working Papers from Department of Economics - University of 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.

Keywords: CAPM; conditional heteroskedasticity; factor models; HC standard errors (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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