Testing the Conditional CAPM using Cross-sectional Regressions
A Multi-task Learning Approach
- verfasst von
- Joachim Grammig, Constantin Hanenberg, Christian Schlag, Jantje Sönksen
- Abstract
In this paper, we introduce a novel representation of the conditional CAPM that allows us to express both the beta and the market premium as functions of option prices. To test our model, we conduct cross-sectional regressions that include the implied beta and other stock characteristics as regressors. We contribute to the existing literature by 1) systematically selecting stock characteristics with a combination of ℓ1- and ℓ2-regularization, known as the multi-task Lasso, and 2) addressing the problem of post-selection inference via repeated sample splitting. Empirically, we find that while variants of the momentum effect lead to a rejection of our model, the implied beta is by far the most important predictor of cross-sectional return variation. The framework is suitable to test other implementations of the conditional CAPM or, more generally, conditional linear factor models with time-varying parameters.
- Externe Organisation(en)
-
Goethe-Universität Frankfurt am Main
Eberhard Karls Universität Tübingen
Universität zu Köln
Leibniz-Institut für Finanzmarktforschung SAFE
- Typ
- Arbeitspapier/Diskussionspapier
- Anzahl der Seiten
- 67
- Publikationsdatum
- 16.04.2024
- Publikationsstatus
- Elektronisch veröffentlicht (E-Pub)
- Elektronische Version(en)
-
https://doi.org/10.2139/ssrn.4788066 (Zugang:
Offen)
