Foreign Exchange Intervention in Two Small Open Economies: The Canadian and Australian Evidence
Bibliographic record
Abstract
We examine intervention by the Bank of Canada and the Reserve Bank of Australia for daily data from 1989 to 1998. Both central banks used to intervene in response to exchange rate volatility and uncertainty that appeared excessive. Volatility is measured via the implied volatility of foreign currency futures options. Uncertainty is proxied by the kurtosis of the implied risk-neutral probability density functions. We also examine the impact from the introduction of inflation targeting. In a departure from other studies of this kind, we explicitly consider the role of commodity futures prices. After all, both countries have been seen by many traders as being commodity currencies. Moreover, we also take into account the impact of a wide range of news events on the intervention practices of both central banks. These additional variables turn out to help explain the effectiveness of intervention. Central bank intervention was largely unsuccessful in both countries though volatility and kurtosis were modestly affected. We also find some notable differences in the impact of inflation targets in both countries on the effectiveness of foreign exchange intervention.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".