Cross-Sectional Estimation Biases in Risk Premia and Ze-ro-Beta Excess Returns
Bibliographic record
Abstract
This paper shows that the classic cross-sectional asset pricing tests tend to suffer from severe risk-premium estimation errors because of small variation in betas. We explain how the conventional approach uses low criteria to validate an asset-pricing model and suffers from the model-misspecification issue because of the complication associated with the zero-beta excess return. We show that the resulting biases in estimates of risk premia and their standard errors are severe enough to lead researchers into inferring incorrect implications about some asset-pricing theories being tested. Further, we suggest that one simple method of mitigating these issues is to restrict the zero-beta excess returns to their theoretical values in the cross-sectional regressions and to conduct the straightforward test of whether the estimated ex-ante risk premia are consistent with the observed ex-post ones. The empirical testing results not only further affirm the higher efficiency of the estimates produces by the suggested method, but also show, contrary to some prior evidence, that the market factor is priced consistently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".