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Record W4386021329 · doi:10.1002/fut.22456

EPU spillovers and sovereign CDS spreads: A cross‐country study

2023· article· en· W4386021329 on OpenAlexaff
Yuting Gong, Zhongzhi He, Wenjun Xue

Bibliographic record

VenueJournal of Futures Markets · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsBrock University
Fundersnot available
KeywordsSpillover effectEndogeneityCredit default swapEconomicsEmerging marketsSovereign creditMonetary economicsCredit riskSovereigntyVector autoregressionFinancial systemBusinessEconomic policyPoliticsMacroeconomicsFinanceEconometricsPolitical science

Abstract

fetched live from OpenAlex

Abstract This paper examines the spillover effect of global economic policy uncertainty (EPU) on sovereign credit default swap (CDS) spreads in a sample of 21 countries. We use a multivariate quantile model to measure EPU spillovers for each country and find that global EPU spillovers have a significant and positive effect on subsequent CDS spreads in both developed and emerging markets. The spillover effect is stronger in developed markets compared to emerging markets. The positive relationship between EPU spillovers and CDS spreads remain significant when controlling for various economic, financial, and political risk factors. Our results are robust to alternative measures of EPU spillovers and sovereign credit risk, across different forecast horizons, and to potential endogeneity resulting from omitted variables.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.255
Teacher spread0.236 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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