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Record W2800009500 · doi:10.34989/sdp-2017-17

Who Pays? CCP Resource Provision in the Post-Pittsburgh World

2021· preprint· en· W2800009500 on OpenAlexaff
Jorge Cruz Lopez, Mark Manning

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsBank of Canada
Fundersnot available
KeywordsSummitBusinessOver-the-counterFinancial systemResource (disambiguation)FinanceMedicineGeography

Abstract

fetched live from OpenAlex

At the Pittsburgh Summit in 2009, G20 countries announced their commitment to clear all standardized over-the-counter (OTC) derivatives through central counterparties (CCPs). Since then, CCPs have become increasingly important and there has been an extensive program of regulatory enhancements to both them and OTC derivatives markets. However, as OTC clearing has grown, tensions have emerged among market participants over CCPs’ traditional model of resource provision through loss mutualization. We argue that most of these tensions can be explained by a misalignment between the policy goal of enhancing financial stability and the delivery of that goal by mandating clearing through CCPs as they are currently organized. Specifically, the traditional model for resource provision makes most CCPs suitable for managing club goods, whereas financial stability is a public good. The key differences between these two types of goods, driven by the wedge between those who pay for them and those who derive the benefits, create the observed tensions. Thus, we propose a framework to analyze the functional elements of a CCP and examine whether an alternative clearing model might be more effective in supporting financial stability. We conclude that some tensions could perhaps be mitigated by unbundling the functions of a CCP and selecting the ownership and funding structure that best suits their individual characteristics. Functions that are critical for the provision of financial stability might imply some form of public sector involvement, whereas other services might lend themselves to a for-profit or traditional club model.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.239
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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