Choosing instruments in managing dollar foreign exchange reserves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two years ago, managers of official foreign exchange reserves were pondering the uncertain but serious prospect of a shrinking stock of outstanding US Treasury securities. This concern reflected the fact that some three quarters of global foreign exchange reserves were held in US dollars, and their management traditionally favoured US Treasury securities. Today, with the US economy growing slowly after a shallow recession, and the effects of discretionary tax cuts being felt, the outstanding stock of Treasury securities is once again expanding. Moreover, while the risk of a war of unknown duration and expense attaches more than usual uncertainty to any forecast of future US deficits, there is little doubt that this expansion will continue for some time. The challenge posed by the gradual disappearance of the outstanding stock of the traditional investment vehicle no longer seems so pressing as it was two years ago. Managers of official foreign exchange reserves no longer face the gradual disappearance of the outstanding stock of their traditional investment vehicle as a given. The pressure to achieve returns in an environment of lower interest rates may nevertheless pose other challenges to reserve managers. It puts the spotlight on reserve managers’ choice of instrument. This note analyses the instruments in which central banks have invested their dollar reserves in recent years and poses three questions: How is the official dollar portfolio invested? How has the choice of instrument evolved over time? And how have recent events, including the return of recession and US fiscal deficits, lower Treasury yields and corporate defaults, altered its evolution?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it