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Credit Default Swaps: Past, Present, and Future

2016· article· en· W1891479151 on OpenAlexaff
Patrick Augustin, Marti G. Subrahmanyam, Dragon Yongjun Tang, Sarah Qian Wang

Bibliographic record

VenueAnnual Review of Financial Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsMcGill University
FundersOne Mind
KeywordsCredit default swapCredit derivativeBusinessLiberian dollarFinancial systemIntermediationFinancial marketAgency (philosophy)Derivatives marketFinanceEconomicsCredit riskFutures contractCredit history

Abstract

fetched live from OpenAlex

Credit default swaps (CDS) have grown to be a multi-trillion-dollar, globally important market. The academic literature on CDS has developed in parallel with the market practices, public debates, and regulatory initiatives in this market. We selectively review the extant literature, identify remaining gaps, and suggest directions for future research. We present a narrative including the following four aspects. First, we discuss the benefits and costs of CDS, emphasizing the need for more research in order to better understand the welfare implications. Second, we provide an overview of the postcrisis market structure and the new regulatory framework for CDS. Third, we place CDS in the intersection of law and finance, focusing on agency conflicts and financial intermediation. Last, we examine the role of CDS in international finance, especially during and after the recent sovereign credit crises.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0040.008
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.234
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations86
Published2016
Admission routes1
Has abstractyes

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