Do Exchange Rates Respond to Day-to-Day Changes in Monetary Policy Expectations? Evidence from the Federal Funds Futures Market
Bibliographic record
Abstract
This paper is the first to utilize the informational content embodied in Federal funds futures contracts for extracting day-to-day changes in expectations of future US monetary policy, in the context of a study of day-to-day exchange rate changes. We analyze more than 12 years of daily exchange rate data and show that continuous day-to-day changes in expectations of future US monetary policy has a significant and systematic impact on day-to-day changes in exchange rates. Our results imply that monetary policy matters for daily exchange rate determination in more ways than merely through infrequent, actual policy changes. Furthermore, when focusing on the actual monetary policy changes, the paper confirms that only the unexpected element of a policy change impacts exchange rates. The presented findings are generally consistent with the notion that exchange rates are forward-looking asset prices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; both teacher heads agree on what is shown here.
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".