A Framework for Macro Stress-Testing the Credit Risk of Commercial Banks: The Case of Vietnam
Bibliographic record
Abstract
In this paper, we assess the capacity of Vietnamese commercial banks to withstand the effects of an increase in credit risk as a result of macroeconomic shocks. Firstly, VAR model is used to estimate the relationship among macro variables (real GDP, real exchange rate, lending interest rate and inflation rate) and from that, macroeconomic scenarios are set up. Next, we employ a GMM model to estimate the relationship between the non-performing loan ratio (credit risk) and macro variables involved in first step. Finally, the new capital requirement ratio (CAR) is recalculated, which is based on the increase in loan provision followed by the rise in non-performing loan. The results show that credit risk which the commercial banks have to face is relatively limited when their risk weighted assets are unchanged. If these numbers, however, increase as banks broaden their lending, all banks’ CAR will reduce remarkably and four large banks will be lack of capital seriously and cannot meet the requirement of Central Bank.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".