Conditional value-at-risk-based optimal partial hedging
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Bibliographic record
Abstract
ABSTRACT In this paper, we consider the problem of optimal partial hedging for a contingent claim subject to a preset hedging budget constraint. Under some technical assumptions on the hedged loss function and the market pricing functional, the optimal partial hedging strategy, which minimizes the conditional value-at-risk (CVaR) of the hedger's total risk exposure, is derived explicitly. Some in-depth analysis is conducted for a utility-based indifference pricing functional. Ample numerical examples are presented to highlight the comparative advantages of the proposed CVaR-based hedging strategy relative to other hedging strategies including expected shortfall hedging, VaR-based hedging strategies and the CVaR hedging strategy of Melnikov and Smirnov. Among these hedging strategies, the numerical examples demonstrate that our proposed CVaR-based hedging is more robust and more effective in terms of managing the tail risk of the hedger's risk exposure.
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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 | 0.000 |
| 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 it