Economic Quarterly—Volume 97, Number 4—Fourth Quarter 2011—Pages 389–413 Strategic Behavior in the Tri-Party Repo Market
Bibliographic record
Abstract
R epo contracts are a kind of collateralized loan that has become pre-dominant in the United States among large cash investors. Thereare several types of repo contracts, such as bilateral delivery-versus-payment repos, interdealer repos, and tri-party repos. A significant portion of repo transactions in the United States take the form of tri-party repos, where a third party (a clearing bank) provides collateral management and settlement services to the borrower and the lender. The tri-party segment of the U.S. repo market is the subject of this article. The tri-party repo market played a significant role during the 2007–2009 global financial crisis. Tri-party repos were, for example, a major source of se-cured funding for Bear Sterns prior to its demise. In March 2008, repo lenders in general, and tri-party repo counterparties in particular, lost confidence in their ability to recoup loans to Bear Stearns and, hence, refused to renew them, asking instead for immediate repayment (Bernanke 2008). To avoid a failure, the Federal Reserve facilitated the acquisition of Bear Stearns by the bank J.P. Morgan Chase. The withdrawal of tri-party repo funding also played a role in the collapse of Lehman Brothers in September 2008. As a result of the events during the crisis, it is now widely believed that the tri-party repo market is subject to serious vulnerability (see, for example, Dudley [2009]). Attesting to this is the fact that in 2009 the NewYork Fed asked a group of senior private U.S. bank officials to form a task force “to address the weaknesses ” in the I would like to thank Jeff Lacker for many stimulating conversations that motivated me to write this article, Jim Peck for answering my game theory questions patiently, and Borys
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.016 |
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".