Credit Default Risk Assessment of Local Government Debts Based on KMV Model
Bibliographic record
Abstract
With the continuing expansion of Chinese local government debts, its credit risk issues raise the public attention. According to the overall statistics data in Chinese Statistic Bureau, there’re various scales of debts exist, undertaken by Chinese prefecture-level cities’ local government. Some of them exceed the alerting level of international line. In an effort to measure the credit default risk level of Chinese local governments, this paper makes a moderate assessment of credit default risk based on modified KMV model. In conditions of a variety of local government revenue, this model calculates the distance from default and default possibility of local government debts under different guarantee proportion. Meanwhile, this paper also explores the variation of local governments’ credit default risk when they use different financial ratio of financing for the construction of urban infrastructure. Finally, we reach the conclusion that the expected default probability shrinks as guarantee proportion raises, and increases as financing proportion raises; under a 40% of guarantee proportion, expected default rates are low with controllable risks; And within a financing proportion of 50%, chances of default as well as risks, are low.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".