A HIGH-FREQUENCY ANALYSIS OF THE EFFECTS OF CENTRAL BANK COMMUNICATION ON THE TERM-STRUCTURE OF INTEREST RATES IN BRAZIL
Bibliographic record
Abstract
This work builds semantic scores using the Google and the Factiva Dow Jones database, based on Lucca and Trebbi's (2011) methodology, in order to quantify the content of the COPOM's statements released by the Central Bank of Brazil shortly after the interest rate's decision and attributing to it a semantic orientation, "hawkish" or "dovish".Using daily and intraday data of swap contracts and DI1 futures contracts, respectively, we find that the content of the BCB's statement affects the yield curve only in the period prior to Tombini's tenure.In addition, we find that the yields respond one-to-one to the interest rate surprise, sometimes more, in the pre-Tombini period even for long term maturities, which we do not see in the period prior to Tombini, where the interest rate surprises affect only the short-to-medium rates.Furthermore, we see an intraday dynamic in the yield responses to the content of the statement in the Tombini period, which give evidence to a delay in its interpretation, differently from what we observe in the previous period.We also find that the interest rate surprises induce changes in the yield curve during the whole time that the market is open for both periods analyzed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".