Collateral Advantage: Exchange Rates, Capital Flows and Global Cycles
Bibliographic record
Abstract
We construct a two-country New Keynesian model in which US government debt has an advantage as a superior collateral asset in the balance sheets of banks.The model can account for the observed response of the US dollar and US bond returns to a global downturn, in particular when the downturn is associated with a global financial crisis.In our model, the U.S. enjoys an "exorbitant privilege" as its government bonds are desired by banks both in the U.S. and abroad as superior collateral.In times of global stress, the dollar appreciates and the "convenience yield" earned by U.S. government bonds increases.There is "retrenchment" -each country reduces its holdings of foreign assets -a critical determinant of which is the endogenous response of prices and returns.In addition, the model displays a U.S. real exchange rate appreciation despite that domestic absorption in the US falls relative to the rest of the world during a global downturn, thus addressing the "reserve currency paradox" highlighted by Maggiori (2017).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".