On the Nature of (Jump) Skewness Risk Premia
Bibliographic record
Abstract
Market skewness risk is priced, but the components of its premium are not fully understood. We propose new trading strategies decomposing the skewness risk premium into jump and leverage effect components, and we analyze the skewness risk premia in the market for S&P 500 index options. We find that the skewness premium is higher when markets are closed than during trading hours, consistently with uncertainty resolution patterns by non-U.S investors; that it increases after left-tail market events; and that it is distinct from the variance premium. Moreover, during trading hours, the skewness premium is dominated by priced jump risk. This paper was accepted by Kay Giesecke, finance. Funding: P. Orłowski acknowledges financial support from the Doc.Mobility program of the Swiss National Science Foundation [Project P1TIP1_161875 “Option portfolio returns and dispersion”]. P. Schneider acknowledges financial support from the Swiss National Science Foundation [Projects 169582 “Model-free asset pricing” and 189086 “Scenarios”]. F. Trojani and P. Orłowski acknowledge financial support from the Swiss National Science Foundation [Project 150198 “Higher order robust resampling and multiple testing methods”] and the Swiss Finance Institute [Project “Term structures and cross-sections of asset risk premia”]. F. Trojani gratefully acknowledges support from the AXA Chair in Socioeconomic Risks of Financial Markets at the University of Turin. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4734 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".