The Determinants of the Volatility of Non-Performing Loans of Tunisian Banks: Revolution Versus COVID-19
Bibliographic record
Abstract
The paper presents the use of the difference GMM, the system GMM and the Panel VAR for the purpose of determining the critical determinants of non-performing loans.The aim of the paper is to point out the factors that explain the volatility of NPLs in a time of crisis.The study focused on a sample of 18 Tunisian banks observed during the period 2008-2018.The paper seeks to identify the impact of crucial macro, microeconomic and governance variables on the NPLs.The results suggest that the deterioration in asset quality can be attributed to both macroeconomic and bank-specific factors.The liquidity risk has a positive and significant correlation with the NPLs of Tunisian banks.The variable "Revolution" presents a positive though not significant relationship with these.Also, the results emphasize the strength of macrofinancial feedback loops in Tunisia.As for the effect of the positive shock of the revolution on the NPL level, we note that it is significant and negative.The decomposition of the sample into two sub-samples: pre-revolution period and post-revolution period allowed showing that the ROA and the ownership structure affect negatively and significantly the NPLs of the banks in the two periods, while the capital affects them positively.It appears that bank-specific factors explain well the volatility of NPLs, especially in the post-revolution period.Finally, by a descriptive study, we have shown that the COVID-19 crisis explains the volatility of the NPLs of Tunisian banks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".