Disaggregating marketplace attitudes toward risk: a contingent-claim-based model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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