MétaCan
Menu
Back to cohort
Record W2052192376 · doi:10.5539/ijef.v6n1p165

The Risk Structural of European Sovereign Credit Default Swap before and after in European Periphery Countries

2013· article· en· W2052192376 on OpenAlexvenueno aff
Işıl Tellalbaşı

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsCredit default swapSovereign creditCredit riskFinancial systemCredit derivativeSovereign defaultDefaultSovereigntyDemiseBusinessDebtEuropean debt crisisEconomicsiTraxxSovereign debtEuropean unionEconomic policyFinanceCredit valuation adjustmentCredit referencePolitical science

Abstract

fetched live from OpenAlex

This study has represented the determinants of sovereign CDS spreads during current sovereign debt crisis in periphery countries namely Ireland, Italy, Portugal and Spain. The period of analysis is between 2008 and 2012 years. After the demise of Lehman Brothers, the sovereign CDS market has attached significant attention and the credit markets have been issue to an unprecedented re-pricing of credit risk. Moreover, Lehman Brothers devastated investor confidence and decrease in the availability of credit. Massive assistance of the banks was heightened public sector deficit. Thus it has led to high level sovereign debt. This means that the risk of default of sovereign became real in periphery countries. This study has been classified three phases. Firstly an analysis of credit default swaps and their use in the financial World. Secondly development of the European periphery economy on a macro level in Portugal, Ireland, Italy and Spain. Finally the statistical approach of ordinary least square is to be analysed. Main purpose of this study will identify sovereign credit default swaps associated with the current sovereign debt crisis.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.295
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Economics and FinanceSame topicCredit Risk and Financial RegulationsFrench-language works237,207