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Improved inference in financial factor models

Beck, Elliot; De Nard, Gianluca; Wolf, Michael (2023). Improved inference in financial factor models. International Review of Economics and Finance, 86:364-379.

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.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Finance
Social Sciences & Humanities > Economics and Econometrics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:July 2023
Deposited On:30 Jun 2023 08:47
Last Modified:29 Oct 2024 02:40
Publisher:Elsevier
ISSN:1059-0560
Additional Information:Bereits in der Working Paper Series / Department of Economics als No. 430 erschienen (https://www.zora.uzh.ch/id/eprint/232341/ ).
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.iref.2023.03.009
Related URLs:https://www.zora.uzh.ch/id/eprint/232341/
Other Identification Number:merlin-id:23575

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