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Record W2950344365 · doi:10.1093/rof/rfy033

Do Credit Default Swaps Mitigate the Impact of Credit Rating Downgrades?

2018· article· en· W2950344365 on OpenAlexafffund
Sudheer Chava, Rohan Ganduri, Chayawat Ornthanalai

Bibliographic record

VenueEuropean Finance Review · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDowngradeCredit ratingCredit default swapBusinessBond credit ratingMonetary economicsDebtFinancial systemFinanceCredit riskEconomicsCredit reference

Abstract

fetched live from OpenAlex

Abstract We find that a firm’s stock price reaction to its credit rating downgrade announcement is muted by 44–52% when credit default swaps (CDSs) trade on its debt. We explore the role of the CDS markets in providing information ex ante and relieving financing frictions ex post for downgraded firms. We find that the impact of CDS trading is more pronounced for firms whose debt financing is more dependent on credit ratings (e.g., those rated around the speculative-grade boundary, those with a higher number of rating-based covenants). Reductions in debt and investment, and the increase in financing costs are less severe for CDS firms than non-CDS firms following an identical credit rating downgrade. Our results suggest that CDSs mute the stock market reaction to a credit rating downgrade by alleviating the financing frictions faced by downgraded firms.

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)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.287
Teacher spread0.241 · 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

Citations40
Published2018
Admission routes2
Has abstractyes

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