Utjecaj premije rizika zemlje na poslovanje banaka u Republici Hrvatskoj
Bibliographic record
Abstract
The aim of this paper is to analyze the impact of the country risk premium on the activity of Croatian banks. Country risk premium is a key determinant of many economic and financial indicators. In most common approach, three measures are identified, so there are government bond yields, goverment bond spread and CDS spread. In fact, backgrounds of those measures are bond market and government bonds. One of the biggest investors in government bonds are commercial banks, which represents the interconnection of sovereign and banking system. In literature, that connection is investigated under the term of sovereign-bank nexus. There are a few transmission channels through which a tensions from government bond markets may be transmitted into the banking system, with effects on the economy as a whole. Recent EU sovereign debt crisis is the best example of how the increased country risk premium negatively affects banks. The model of multiple linear regression is specificied to assess the impact of country risk premium on specific banking indicators in Croatia. Analysis covered period from last quarter of 2011 to first quarter of 2020. Results of the model show that country risk premium has an impact on both, the interest rates on deposits and interest rates on loans. Moreover, there is also an impact on bank lending. It can be concluded, based on the analysis, that 1 percentage point increase of spread , which represents country risk premium, is associated with 3 basis points increase in the interest rates on overnight deposits and 35 basis points increase in the interest rates on deposits with agreed maturity. Also, 1 percentage point increase in sovereign spread is associated with a 62 b.p., 102 b.p. and 60 b.p. increase, respectively, in the intrerest rates on loans to households for house purchase, consumer credit to households and loans to firms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".