Improved Inference in Financial Factor Models

28 Pages Posted: 12 May 2022

See all articles by Elliot Beck

Elliot Beck

University of Zurich - Department of Banking and Finance; Swiss National Bank

Gianluca De Nard

University of Zurich - Department of Banking and Finance

Michael Wolf

University of Zurich - Department of Economics

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.

Suggested Citation

Beck, Elliot and De Nard, Gianluca and Wolf, Michael, Improved Inference in Financial Factor Models. Available at SSRN: https://ssrn.com/abstract=4107472 or http://dx.doi.org/10.2139/ssrn.4107472

Elliot Beck

University of Zurich - Department of Banking and Finance ( email )

Schönberggasse 1
Zürich, 8001
Switzerland

Swiss National Bank ( email )

Research
Fraumuensterstr. 8
Zuerich, 8022
Switzerland

Gianluca De Nard (Contact Author)

University of Zurich - Department of Banking and Finance ( email )

Zürichbergstrasse 14
Zürich, Zürich CH-8032
Switzerland

HOME PAGE: http://denard.ch

Michael Wolf

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zurich, 8032
Switzerland

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