Counterparty Risk and the Establishment of the New York Stock Exchange Clearinghouse
Bibliographic record
Abstract
Heightened counterparty risk during the recent financial crisis has raised questions about the role clearinghouses play in global financial stability.Empirical identification of the effect of centralized clearing on counterparty risk is challenging because of the co-incidence of macro-economic turbulence and the introduction of clearinghouses.We overcome these concerns by examining a novel historical experiment, the establishment of a clearinghouse on the New York Stock Exchange (NYSE) in 1892.During this period the largest NYSE stocks were also listed on the Consolidated Stock Exchange (CSE), which already had a clearinghouse.Using identical securities on the CSE as a control, we find that the introduction of clearing reduced annualized volatility of NYSE returns by 90-173bps and increased asset values.Prior to clearing, shocks to overnight lending rates reduced the value of stocks on the NYSE, relative to identical stocks on the CSE, but this was no longer true after the establishment of clearing.We also show that at least ½ of the average reduction in counterparty risk on the NYSE is driven by a reduction in contagion risk -the risk of a cascade of broker defaults.Our results indicate that clearing can cause a significant improvement in market stability and value through a reduction in network contagion and counterparty risk.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".