When Lower Risk Increases Profit: Competition and Control of a Central Counterparty
Bibliographic record
Abstract
We model the behavior of dealers in Over-the-Counter (OTC) derivatives markets where a small number of dealers trade with a continuum of heterogeneous clients (hedgers). Imperfect competition and (endogenous) default induce a familiar trade-off between competition and risk. Increasing the number of dealers servicing the market decreases the price paid by hedgers but lowers revenue for dealers, increasing the probability of a default. Restricting entry maximizes welfare when dealers’ efficiency is high relative to their market power. A Central Counter-Party (CCP) offering novation tilts the trade-off toward more competition. Free-entry is optimal for all level of dealers’ efficiency if they can constrain risk-taking by its members. In this model, dealers can choose CCP rules to restrict entry and increase their benefits. Moreover, dealers impose binding risk constraints to increase revenues at the expense of the hedgers. In other words, dealers can use risk controls to commit to a lower degree of competition. These theoretical results provide one rationalization of ongoing efforts by regulators globally to promote fair and risk-based access to CCPs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".