Comparative risk aversion in RDEU with applications to optimal underwriting of securities issuance
Bibliographic record
Abstract
We provide a characterization of comparative weak risk aversion and comparative RDEU risk aversion for RDEU preferences and, in particular, we correct a claim made by Quiggin (1993) regarding comparative RDEU risk aversion. We then apply the analysis of comparative risk aversion to a problem of optimal design of underwriting contracts in securities issuance. Specifically, in public offerings of equity, an investment banking firm (the underwriter) plays an insurance role: through the underwriting contract, the issuing firm transfers the issue risk to the underwriter, as would an insured to an insurer. We extend a classical model proposed by Mandelker and Raviv (1977) to situations where the issuing firm and the underwriter have RDEU preferences. Assuming that the issuing company’s and the underwriter’s utility functions are concave and linear, respectively, and that either the underwriter is risk neutral or both the issuing company and underwriter are strongly risk averse, we show that a firm-commitment contract is optimal if and only if the issuing company’s probability weighting function dominates the underwriter’s.
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.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".