Idiosyncratic Jump Risk Matters: Evidence from Equity Returns and Options
Bibliographic record
Abstract
Abstract The recent literature provides conflicting empirical evidence about the pricing of idiosyncratic risk. This paper sheds new light on the matter by exploiting the richness of option data. First, we find that idiosyncratic risk explains 28% of the variation in the risk premium on a stock. Second, we show that the contribution of idiosyncratic risk to the equity premium arises exclusively from jump risk. Third, we document that the commonality in idiosyncratic tail risk is much stronger than that in total idiosyncratic risk documented in the literature. Tail risk thus plays a central role in the pricing of idiosyncratic risk. Received May 15, 2017; editorial decision September 12, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished code and an Internet Appendix, which are available on the Oxford University PressWeb site next to the link to the final published paper online.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".