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Record W3121908232 · doi:10.2139/ssrn.1713325

Are All Credit Default Swap Databases Equal?

2010· preprint· en· W3121908232 on OpenAlexaff
Sergio Mayordomo, Juan Ignacio Peña, Eduardo S. Schwartz

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMarket liquidityDatabaseCredit default swapDatabase transactionComparabilityPrice discoveryRobustness (evolution)Swap (finance)BusinessEconomicsEconometricsFinancial economicsMonetary economicsActuarial scienceCredit riskComputer scienceFinanceMathematics

Abstract

fetched live from OpenAlex

In this study we compare the five major sources of corporate Credit Default Swap prices: GFI, Fenics, Reuters, CMA, and Markit, using the most liquid single name 5-year CDS of the components of the leading market indexes, iTraxx and CDX for the period from 2004 to 2010. We find systematic differences between the data sets implying that deviations from the common trend among prices in the different databases are not purely random but are explained by idiosyncratic factors as well as financial institutions financing costs, global risk, and other trading factors. The lower is the amount of transaction prices available the higher is the deviation among databases. The CMA database quotes lead the price discovery process in comparison with the quotes provided by other databases. Moreover, we find that there is not a full consistency among databases in the results of price discovery (causality) analysis between stock and CDS returns.

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)0.008
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.008
Science and technology studies0.0020.004
Scholarly communication0.0120.025
Open science0.0030.004
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0200.003

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 head0.054
GPT teacher head0.275
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 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

Citations18
Published2010
Admission routes1
Has abstractno

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