The Determinants of Corporate CDS Spreads: Do Equity Liquidity and Jump Matter?
Bibliographic record
Abstract
This paper investigates whether equity liquidity and stock return jump are important determinants for the Korean corporate CDS spreads. The previous studies mainly have examined the determinants of CDS spread time series levels, whereas this study focuses on the determinants of changes or differences of CDS spread time series as well as the effecting factors of cross-sectional variations. Using monthly averaged CDS quotes for 29 firms from Jan. 2005 to Nov. 2012, we first demonstrate that the explanatory power for CDS spread changes is improved to about 39% by adding both credit risk-related market variables and firm-level jump variables, contrary to the low explanatory power (approximately 21%) reported by the previous study. However, since the principle component analysis for residuals from the regression shows that a common risk factor exists, it is possible that additional important factor remains. In addition, we demonstrate that stock return volatility is a robust variable to explain the cross-sectional differences in CDS spreads. We also find that the equity liquidity is a robust and significant factor for the cross-sectional differences in CDS spreads after the global financial crisis period. The result implies that, after the recent crisis, investors more actively considered equity illiquidity costs when they hedged their CDS exposures by stocks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".