Liquidity and Central Clearing: Evidence from the CDS Market
Bibliographic record
Abstract
An international initiative to increase the use of central clearing for OTC derivatives emerged as one of the reactions to the 2008 financial crisis. The move to central clearing is a fundamental change in the structure of the market. Central clearing will help control counterparty credit risk, but it also has potential implications for market liquidity. We analyze the relationship between liquidity and central clearing using information on credit default swap clearing at ICE Trust and ICE Clear Europe. We find that the central counterparty chooses the most liquid contracts for central clearing, consistent with liquidity characteristics being important in determining the safety and efficiency of clearing. We further find that the introduction of central clearing is associated with a slight increase in the liquidity of a contract. This is consistent with two countervailing effects. On one hand, central clearing will likely increase collateral requirements relative to the pre-reform bilaterally-cleared market, thereby increasing clearing costs and possibly reducing the liquidity of the market. On the other hand, improved management of counterparty credit risk, increased transparency and operational efficiencies at central counterparties could bring more competition into OTC derivative markets and serve to increase liquidity.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".