Macroprudential Policy and Central Bank Communication
Bibliographic record
Abstract
In response to the financial crisis of 2007-2010, many central banks are getting involved in macroprudential supervision. Central bank communication will constitute a central policy tool for that purpose. The paper asks how such communication will affect financial markets, exploiting the fact that many central banks have had some financial stability role in the past, and have communicated extensively on this through the publication of Financial Stability Reports (FSRs) and financial stability-related statements. Building a unique dataset, it provides an empirical assessment of the financial market reactions to more than 1000 releases of FSRs and speeches in 36 countries over the past 14 years. The findings suggest that FSRs have a significant and potentially long-lasting effect on stock market returns, and also tend to reduce market volatility. Speeches and interviews, in contrast, have little effect on market returns and tend to increase volatility during tranquil times, but can have a substantially larger effect during periods of financial stress. Moreover, central bank communication can affect markets even when leaning against asset price booms. The findings underline the importance of differentiating between communication tools and content when designing a communication strategy on macroprudential issues.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".