The short-term reaction of financial markets to the U.S. trade tariff announcement
Bibliographic record
Abstract
We analyze the reaction of global markets to the US tariff announcement of April 2, 2025. The study utilizes daily prices from 20 stock market indices that account for the largest US trade. The indices are obtained from S&P Capital IQ Pro and span the period from January 02, 2024, to May 28, 2025. The aim of the study is to evaluate the impact of the tariff announcement on the market volatility levels, correlation patterns, and market connectedness. To ensure robustness of the findings, we employ methodological triangulation, incorporating the combined use of network theory and visual techniques. The GARCH model is used to show the change in conditional volatility after the announcement. Correlation heat maps and minimum spanning tree graphs are used to interrogate the correlation among the indices. The Quantile Vector Autoregression (QVAR) model is utilized to assess changes in connectedness. The study contributes to our understanding of how global markets respond to tariffs, and the importance of emerging markets as volatility transmitters. The results show that conditional volatility increased roughly two-fold post-tariff, the correlations have become significantly higher post-tariff, and markets are more interconnected. Brazil, Canada, South Africa and Vietnam emerge as the main transmitters, possibly due to regional trade shifts, while the US become less central. These findings indicate an increase in systemic risk in the global markets and underscore the need for de-escalation of the trade conflict and the pursuit of a negotiated resolution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".