Makroekonomiczne czynniki ryzyka kredytowego w sektorze bankowym w Polsce
Bibliographic record
Abstract
The article explores key micro- and macroeconomic factors with an impact on credit risk and analyzes the credit risk model prevalent in Poland’s banking sector. Credit risk is one of the most important risks in the banking sector, the author says. He adds that risk management should be subject to strict owner control and regulatory and supervisory measures. On the basis of quarterly data for a period from the first quarter of 1997 to the second quarter of 2013, Wdowiński estimated an error correction model for aggregate credit risk in Poland, as measured by the proportion of non‑performing loans (NPLs) in total loans. The key macroeconomic factors considered by the author were GDP, the interest rate, the unemployment rate, and the exchange rate. An ex post simulation for the 2008–2012 period, based on an adverse macroeconomic scenario for Poland, showed that such a scenario could lead to a marked increase in credit risk for non‑financial enterprises and households, Wdowiński says. As a result of this scenario, the banking sector could be affected by a significant decline in activity and its financial position would deteriorate. This would mean fewer investment opportunities for banks and a decline in their capital position, which would reduce their ability to absorb losses. Such a situation, the author concludes, could lead to “second‑round” effects based on limiting financing for the real economy due to increased credit risk and increased lending margins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".