Credit contagion channel and its consequences via the standard portfolio credit risk model
Bibliographic record
Abstract
Using actual default events for all listed firms of 30 economies over the period from the first quarter of 2000 to the second quarter of 2011 and the technique of particle filtering and smoothing with MCMC, we find strong evidence that defaults of small-high-yield firms infect large-high-yield firms which, in turns, generate a feedback contagious effect back onto small-high-yield firms. We demonstrate that this type of credit contagion has a significant impact on the infected group's defaults and tail estimates of portfolio loss. All high-yield groups have systematic risk factors that exhibit a strong AR(1) effect which is an evidence of within group defaults clustering. All investment grade firms are not affected by this channel of credit contagion. In general, the lower grade high-yield firms have a higher default threshold measured in terms of asset value. Small firms are more vulnerable to default than large firms and their defaults could serve as an early warning signal for the system-wide credit contagion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".