Details
| Original language | English |
|---|---|
| Number of pages | 67 |
| Publication status | E-pub ahead of print - 16 Apr 2024 |
| Externally published | Yes |
Abstract
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2024.
Research output: Working paper/Preprint › Working paper/Discussion paper
}
TY - UNPB
T1 - Testing the Conditional CAPM using Cross-sectional Regressions
T2 - A Multi-task Learning Approach
AU - Grammig, Joachim
AU - Hanenberg, Constantin
AU - Schlag, Christian
AU - Sönksen, Jantje
PY - 2024/4/16
Y1 - 2024/4/16
N2 - 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.
AB - 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.
U2 - 10.2139/ssrn.4788066
DO - 10.2139/ssrn.4788066
M3 - Working paper/Discussion paper
BT - Testing the Conditional CAPM using Cross-sectional Regressions
ER -