Global Risk Factors and Their Impacts on Interest and Exchange Rates: Evidence from ASEAN+4 Economies
Bibliographic record
Abstract
This paper revisits the international finance trilemma by analyzing how different monetary policy objectives and exchange rate regimes shape the transmission of global risk shocks. Using a structural vector autoregressive model with exogenous variables (SVARX), we examine the monetary policy responses and exchange rate fluctuations of ASEAN+4 economies—China, Japan, Korea, and Hong Kong—to external shocks including U.S. monetary policy changes, oil price fluctuations, global policy uncertainty, and financial risk during 2010–2022. Economies are grouped according to their trilemma configurations: floating exchange rates with free capital flows, fixed exchange rates, and capital control regimes. Our findings broadly support the trilemma hypothesis: fixed-rate economies align with U.S. interest rate movements, capital control economies retain greater monetary autonomy, and open, floating regimes show partial responsiveness. More importantly, monetary responses vary by global shock type: U.S. monetary policy drives the most synchronized policy reactions, while oil price and uncertainty shocks produce more heterogeneous outcomes. Robustness checks include alternative model specifications, where global shocks are treated as endogenous, and extensions, such as using Japan’s monetary base as a proxy for unconventional monetary policy. These results refine the empirical understanding of the trilemma by showing that its dynamics depend not only on institutional arrangements but also on the nature of global shocks—underscoring the need for more tailored and, where possible, regionally coordinated monetary policy strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".