Monetary policy spillovers in a fragmented world: the role of geopolitical risk pre- and post-COVID-19 pandemic
Bibliographic record
Abstract
Purpose This paper investigates the influence of geopolitical risks on the dynamic spillover of monetary policies among the United States, Canada, Australia, New Zealand, Japan and Switzerland from 1995 to 2023. Design/methodology/approach The time-varying parameter vector autoregressive (TVP-VAR) method is used to investigate the dynamic interconnectedness of monetary policy across the six countries. In addition, ordinary least squares (OLS) regression is applied to assess the influence of geopolitical risk on the transmission of international monetary policies, particularly before and after the COVID-19 pandemic. Findings Our study shows a moderate interdependence between the monetary policies of the examined countries. In the network, the monetary policies of the United States, Japan and Australia are transmitters, while Canada, New Zealand and Switzerland are receivers. In addition, geopolitical risks positively impact monetary policy. However, these impacts have turned negative in the post-COVID-19 period. Research limitations/implications These results suggest that policymakers should account for the spillover of monetary policies from other economies during the policy implementation process. Practical implications These findings may guide monetary policymakers in considering rising geopolitical risks. Originality/value This study enhances the theoretical understanding of monetary policy spillovers by illustrating the transmitting roles of major economies within a global network. Moreover, while existing research often examines monetary policy as an isolated phenomenon, this study demonstrates how such risks influence cross-country monetary policy spillovers differently between the pre- and post-COVID-19 periods. Thus, this study improves our understanding of monetary policy adaptability in a globalized world.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".