Do the Exchange Rates Converge Among Fragile Market Economies? New Evidence from LM and RALS-LM Unit Root Tests
Bibliographic record
Abstract
Global financial integration causes the economic consequences of economic crises, wars, or pandemics to be felt more in developing countries and triggers high exchange rate volatilities in these economies. In such an integrated financial environment, it is an interesting research domain how and why the exchange rate volatilities of countries are not affected similarly but tend to diverge from each other. This study investigates whether the exchange rate volatilities of fragile market economies converge in the stochastic convergence framework. To answer this question, we analyzed the stochastic behavior of the series using the traditional and structural break unit root tests besides RALS unit root tests, which consider the information of non-normal errors. The discussions regarding the size and power properties of test procedures in the unit root testing literature have formed a crucial part of the implications of the test results. In light of these discussions, we conclude that the stochastic convergence assumption is valid for Brazil, South Africa, India, and Hungary, whereas it is not valid for Argentina, Mexico, and Türkiye. The policy implications of our findings are that fragile market economies have different fragility levels among themselves and countries with high fragility levels show higher volatility than others.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".