The Impacts of Financial Crisis on Sovereign Credit Risk Analysis in Asia and Europe
Bibliographic record
Abstract
We investigate the nature of sovereign credit risk for selected Asian and European countries based on a set of sovereign CDS data over an eight-year period that includes the episode of the 2008-2009 global financial crisis. The principal component analysis results indicate that there exists strong commonality in sovereign credit risk among the countries studied in this paper following the crisis. In addition, the regression results show that commonality is importantly associated with both local and global financial and economic variables. There are also important differences in the sovereign of credit risk behavior between Asian and European countries. Specifically, we find that foreign reserve, global stock market, and volatility risk premium, affect Asian and European sovereign credit risks in the opposite direction. Lastly, we model the arrival rates of credit events as a square-root diffusion process from which a pricing model is constructed and estimated over pre and post-crisis periods. The resulting model is used to decompose credit spreads into risk premium and credit-event components. For most countries in our study, credit-event components weight more than risk-premiums, suggesting that, in the long term, investors are perhaps more concerned with the prospect of sovereign-specific credit events than systemic sovereign credit risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".