Senior official speeches and severe price discontinuities in the foreign exchange market
Bibliographic record
Abstract
Purpose This study aims to better understand the effects of speeches as a valuable communication tool for central banks. It extends the analysis of the effects of public speeches on jumps to determine whether individual speakers matter partly because of their name, position or institution. Design/methodology/approach This study detects intraday jumps using a robust-to-jump volatility estimator that accounts for deterministic seasonality. As a result, this study removes spurious jumps that occur when volatility is high and consider the relatively small jumps that occur when volatility is low. After identifying jumps, this study examines their reactions to senior official speeches and macroeconomic news surrounding the US and European Union (EU) financial crises. Findings Despite having the most influential individual speakers, this study finds that the impact of the Federal Reserve (Fed) and European Central Bank (ECB) is mitigated because the two institutions have a relatively small impact on currency jumps. This finding shows that the speaker’s name is more important than his or her institution affiliation. While the Federal Reserve Bank President and Chief Executive, as well as ECB board members, significantly reduce jump sizes, particularly during the EU crisis period, both the Fed Chairman and the ECB President increase the magnitude of the jump in both the US crisis and noncrisis periods, contributing to market instability. Practical implications The implications of the results include international portfolio management, currency derivatives pricing and hedging, risk management and market efficiency. Originality/value The findings contribute to a better understanding of the effects of senior official speech attributes on currency jumps in various economic states. The results raise questions about the speaker’s name, institution and position’s effectiveness in calming markets and reducing uncertainty.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".