Real-Time Effects of Central Bank Interventions in the Euro Market
Bibliographic record
Abstract
This paper investigates the real-time effects of foreign exchange intervention using official intraday intervention data provided by the Danish central bank. Denmark is currently pursuing an active intervention policy under the provisions of the Exchange Rate Mechanism (ERM II) and intervenes on a discretionary basis when considered necessary. Prior participation in ERM II is a requirement for adoption of the Euro. Therefore, our study is of particular relevance for the new European Union member states that are either currently participating in ERM II or expected to do so at a later date as well as for Denmark. Our analysis employs the two-step weighted least squares estimation procedure of Andersen, Bollerslev, Diebold and Vega (2003) and an array of robustness tests. We find that intervention exerts a statistically and economically significant influence on exchange rate returns when the direction of intervention is consistent with fundamentals and intervention is carried out during a period of high exchange rate volatility. We also show that the exchange rate does not adjust instantaneously to the unannounced and discretionary interventions under study. We conclude that intervention can be an important short-term policy instrument for exchange rate management
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".