Variance Risk-Premium Dynamics: The Role of Jumps
Why this work is in the frame
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Bibliographic record
Abstract
Using high-frequency stock market data and (synthetic) variance swap rates, this paper identifies and investigates the temporal variation in the market variance risk-premium. The variance risk is manifest in two salient features of financial returns: stochastic volatility and jumps. The pricing of these two components is analyzed in a general semiparametric framework. The key empirical results imply that investors' fears of future jumps are especially sensitive to recent jump activity and that their willingness to pay for protection against jumps increases significantly immediately after the occurrence of jumps. This in turn suggests that time-varying risk aversion, as previously documented in the literature, is primarily driven by large, or extreme, market moves. The dynamics of risk-neutral jump intensity extracted from deep out-of-the-money put options confirms these findings. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.
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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 it