Determinants and Transmission Channels of Financial Cycle Synchronization in EU Member States
Bibliographic record
Abstract
This paper investigates the determinants and transmission channels underlying the synchronization between financial and business cycles across European Union (EU) member states. For the empirical approach, we combine frequency-domain filtering techniques with spillover index analysis to track cross-country macro-financial interlinkages. We measure financial cycle correlations and spillovers in terms of common exposures to trade linkages, overlapping systemic risk episodes, and bilateral financial claims. An important finding is that financial and business cycles tend to move together, largely due to shared macro-financial conditions and systemic stress episodes. While the data reveal strong co-movement between these cycles, the analysis does not imply a specific direction of causality. In particular, it remains possible that shifts in financial conditions can amplify or even precede business-cycle fluctuations, as seen during major crises. The focus of this study is, therefore, on the interdependence and synchronization of these cycles rather than on causal sequencing. The analysis combines complementary filtering and variance-decomposition methods to quantify the interdependencies shaping EU financial stability, providing a basis for enhanced macroprudential policy coordination. The policy implications for macroprudential authorities entail taking into account cross-border effects and spillovers when implementing instruments for taming the financial cycle.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".