Determinants of Credit Default Swap Spreads: A Four-Market Panel Data Analysis
Bibliographic record
Abstract
This paper attempts to elucidate whether firm performance and macroeconomic conditions play a significant role in explaining credit default swap (CDS) spreads. Our panel dataset covers 112 reference entities in four markets (South Korea, Hong Kong, France, and Germany) for the period 2001-12. Overall, our results suggest that market value indicators (Tobin’s Q, stock market returns, and the interest rate) appear to be more important than book value indicators (i.e., ROA, ROE, and the GDP growth rate) in determining CDS spreads. Moreover, Asian CDS markets are shown to be more sensitive to both GDP and stock market volatility, than the two European markets. Finally, the 2007-09 global financial crisis may have significantly affected the CDS market as a whole, but it generally did not affect the individual markets. These results are robust to various model specifications. This paper contributes to the understanding of CDS determinants at firm-, economy-, and market-level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".