PRELIMINARY DRAFT: PLEASE DO NOT CIRCULATE OR QUOTE
Bibliographic record
Abstract
An objective function is a key component of a strategic portfolio management model used to determine optimal allocations of assets and possibly their associated liabilities over some investment horizon. This paper discusses perspectives and investment philosophies for the management of foreign reserves, and investigates how to translate the three common policy objectives for reserves (liquidity, safety, and return) into objective functions for strategic reserve management. The paper identifies stochastic programming as a practically advantageous modelling framework to capture the objectives of foreign reserves management, and concludes with an illustration of a strategic reserve management model that trades off expected net returns with costs and liquidity issues related to a potential liquidation of a portion of the portfolio. I would like to thank Oumar Dissou, Jesus Sierra Jimenez, Philippe Muller, Miguel Molico, Greg Bauer, Francisco Rivadeneyra, and Zahir Antia from the Bank of Canada and Jerome Kreuser from RisKontrol Group GmbH for valuable ideas and helpful discussions, and the library staff at the Bank of Canada for excellent research assistance. The views expressed in this paper are mine and not necessarily those of the Bank of Canada. I retain any and all responsibility for errors, omissions, and inconsistencies that may appear in this work.
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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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.556 | 0.390 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".