Stress Testing of Commercial Banks’ Exposure to Credit Risk: A Study Based on Write-off Nonperforming Loans
Bibliographic record
Abstract
This study introduced a stress-testing model with a dummy variable that refers to write-off non-performing loans (NPL) by Agricultural Bank of China. A new variable Y that indicated the rate of NPL in major national commercial banks in terms of logit transformation was applied to test stress tolerance. This article built a regression model on the basis of four explanation variables: the growth rate of GDP, indicator of customer price, the growth rate of supplying nominal currency and indicator of house price. Then we took advantages of VAR model to establish the relationship between variables. Based on the model, diverse scenario was set up to conduct stress test to NPL of commercial banks. The test covered four quarters and discovered that lower growth rate of GDP, slump in CPI, slowdown in supply of nominal currency and surging price of house are in charge of short-term increase in non-performing loans. From long-term perspective, the commercial banks would initiate internal system to mitigate the shock from volatile macro factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".