Disaggregating marketplace attitudes toward risk: a contingent-claim-based model
Bibliographic record
Abstract
With a view to providing economic interpretations of temporal changes in Risk-Neutral Probability Distributions (RNPDs), this article estimates RNPDs from option prices, then studies the expected excess returns on a fixed-strategy reference portfolio constructed from RNPD-defined contingent claims. It disaggregates the reference portfolio into an investment, an insurance and a certainty component, each containing one type of contingent claim (having positive, negative or zero expected excess return, respectively). The disaggregation provides a convenient way of operationalizing Markowitz's semi-variance measures, one for upside potential and one for downside risk. Our empirical tests show that the pricing of investment-oriented claims is related to both S&P index growth and volatility, but the pricing of insurance-oriented claims is related only to index volatility. Moreover, the relative importance of insurance earnings to total earnings appears principally to be related to volatility. Thus our analyses show that investment and insurance claims are priced differently in the marketplace, and the different pricing effects can be identified by disaggregating the reference portfolio returns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".