The Replacement of Safe Assets: Evidence from the U.S. Bond Portfolio
Bibliographic record
Abstract
The expansion in financial sector 'safe' assets, largely in the form of structured products from the U.S. and the Caribbean, in the lead-up to the global financial crisis has by now been fairly well documented. Using a unique dataset derived from security-level data on U.S. portfolio holdings of foreign securities, we show that since the crisis, it is mostly the foreign financial sector that appears to have met U.S. demand for safe and liquid investment assets by expanding its supply of debt securities. We also find a strong negative correlation between the foreign share of the U.S. financial bond portfolio and measures of U.S. safe assets availability: providing evidence on the importance of foreign-issued financial sector debt as a substitute when U.S. issued 'safe' assets are scarce. Furthermore, although U.S. investors continue to tap foreign financial markets for 'safe' assets, we show that the type of foreign financial debt that fills this portfolio niche post-crisis is quite different than pre-crisis. Post-crisis, we find that U.S. investors have replaced offshore-issued structured securities with high-grade U.S. dollar-denominated financial debt issued from a small group of OECD countries (most notably Australia and Canada). Lastly, these developments have led to a decline in home bias in the U.S. financial bond portfolio that we are able to document for the first time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".