Dislocations in the Won-Dollar Swap Markets during the Crisis of 2007–2009
Bibliographic record
Abstract
During the 2007–2009 international financial crisis, many countries experienced dislocations in their foreign exchange (FX) swap markets and cross-currency swap markets (see Baba et al., 2012). 1 When foreign banks’ lending to these countries contracted sharply around the fourth quarter of 2008, domestic banks faced difficulties in borrowing in the interbank market as well as much higher costs in obtaining short-term dollar (or euro/Swiss franc in central and eastern Europe) financing through FX swaps. 2 In particular, many of these banks experienced an abrupt drop in gross international claims, which are the sum of cross-border claims in all currencies and local claims in foreign currencies of international banks. 3 To ameliorate the dislocations in their FX swap and cross-currency swap markets, central banks in western Europe (Denmark, Sweden, Switzerland, the United Kingdom, and the euro area [for the European Central Bank]), North America (Canada), Asia (India, Japan, Korea, and Singapore), Latin America (Brazil, Chile, and Mexico), central and eastern Europe (Poland and Hungary), and the Pacific (Australia and New Zealand) either used their own foreign reserves or established swap lines with the US Federal Reserve (Fed) or other central banks. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".