The Borrowing Costs of Selected Countries of the European Union – the Role of the Spillover of External Shocks
Bibliographic record
Abstract
During the recent European public finance crisis the reactions of policy makers were largely motivated by the mounting spreads in the financial markets. But it is not clear to what extent risk premia on the markets really reflect the dynamics of economic fundamentals and to what extent they are determined by other factors. Bearing in mind that market sentiment can trigger divergence in spreads from the levels implied by the fundamentals, this paper draws attention to the importance of the spillover of external shocks and financial contagion on the price of borrowing in selected EU countries and in Croatia. The analysis carried out shows that the measure of spillover and contagion employed in the paper was the dominant factor in explaining risk premia during the recent crisis of public finances. The results also hold true for the levels and for the variability of spreads. From this point of view the spreads relating to Croatia are no exception – the variations in them were much higher than those that could be expected to derive from the dynamics in fundamentals. Nevertheless, it has to be said that such results do not imply that markets ignore movements in fundamentals. Thus a decomposition of spreads in Croatia over the last two years of the sample observed (from the second quarter of 2010 to the second quarter of 2012) indicates a constant rise of the adverse impact of fundamentals on Croatian spreads, which does not hold good for other countries of Central and Eastern Europe, which are often used as comparisons when analysing the risk associated with Croatia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".