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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Improved inference in financial factor models |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | International Review of Economics and Finance |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 1059-0560 |
| Volume | 86 |
| Page Range | 364 - 379 |
| Date | 2023 |
| Abstract Text | 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. |
| Related URLs | |
| Digital Object Identifier | 10.1016/j.iref.2023.03.009 |
| Other Identification Number | merlin-id:23575 |
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| Additional Information | Bereits in der Working Paper Series / Department of Economics als No. 430 erschienen (https://www.zora.uzh.ch/id/eprint/232341/ ). |