Selective Swap Arrangements and the Global Financial Crisis: Analysis and Interpretation
Bibliographic record
Abstract
The onset of the US credit crisis in 2008, and its rapid globalization induced the FED to extend unprecedented swap-lines of 30 billion dollars to four emerging markets, and the proliferation of other cross-countries selective swap arrangements. This paper explores the logic for these arrangements, focusing on the degree to which financial and trade linkages, financial openness and credit risk history account for discerning the formation of swap arrangements to EMs. We also study the impact of the formation of these credit lines on the exchange rate and the financial spreads of the relevant countries. We find that exposure of US banks to EMs is the most important selection criterion for explaining the 'selected four' swap-lines. This result is consistent with the outlined model, where we show that in circumstances of unanticipated deleveraging, emergency swap-lines may prevent or mitigate costly liquidation today, allowing investment projects to reach maturity and providing positive option value to both the source and the recipient countries. The FED swap-lines had relatively large short-run impact on the exchange rates of the selected EMs, but much smaller effect on the spreads (measured relative to that of other EMs that were not the recipients of swap-lines). Specifically, non-swap countries saw an average depreciation of 0.15% on the day after swap announcement, but swap countries saw their exchange rate appreciate on average, by about 4%. Yet, all the swap countries saw their exchange rate subsequently depreciate to a level lower than pre-swap rate, calling into question the long-run impact of the arrangements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".