Troubles with the Chf Loans in Croatia: The Story of a Case Still Waiting to Be Closed
Bibliographic record
Abstract
In numerous Central and Eastern European (CEE) countries, the global financial crisis as well as the unpegging of the foreign exchange rate of the Swiss franc (CHF) against the euro amplified the repayment troubles of households with the outstanding CHF-linked debt. In Croatia, the CHF loans were approved mainly as mortgages to unprotected and subprime household borrowers without sufficient credit capacity for long-term euro-linked loans, which also contained a possibility of an incremental interest rate change, i.e., the so-called administrative interest rate. This article aims to disclose the reasons behind the credit boom of these loans, the unsustainable CHF debt hardship that the household sector consequently faced, and how it was/could have been resolved, with the Croatian banking sector at the center of the research. Although the CHF case of Croatia has some specificities concerning the prudential regulation and government-sponsored loan conversion, the findings about the supply and demand determinants of the CHF credit boom, as well as a critical assessment of the Croatian government and central bank interventions, might be useful for timely noticing universal threats from the exotic currency-linked loans for the systemic risk and financial stability, and for minimizing the negative externalities from probable debt relief measures. Based on the descriptive and univariate statistics conducted on Bloomberg and the Croatian National Bank (CNB) data, it was found that interest rate differentials and carry trading behavior were the main reasons for the rapid CHF credit growth in Croatia. Nevertheless, according to the financial experts’ opinions obtained via a questionnaire survey, and the court verdicts reached since, the financial consumer protection when contracting these loans was severely violated, which implies that the central bank must enhance its consumer protection role. By adopting a single-country and holistic approach, this is the first paper that deals with the socioeconomic dynamic of the CHF credit default issues in Croatia, which might be interesting as a case study or for making comparison with other CEE countries that have been coping with negative consequences of Swiss francization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| 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".