Liquidity, risk, and return: specifying an objective function for the management of foreign reserves
Bibliographic record
Abstract
Countries hold foreign reserves for various reasons, which include foreign‐exchange intervention and liquidity provision. To ensure that reserves can meet their objectives, reserve managers employ strategic models to determine the optimal allocations of assets and, possibly, their associated liabilities over some investment horizon. An objective function is a key component of such models, and its specification is challenging in view of the specialized function of foreign reserves as insurance in crises. To this end, the author investigates how to translate the three common policy objectives for foreign reserves (liquidity, safety, and return) into objective functions for strategic reserves management. A strategic reserves management model is illustrated that trades off expected net returns with costs and liquidity issues related to a potential liquidation of a portion of the portfolio. The model is cast in a stochastic programming framework. Results indicate that liquidity preference affects both the initial asset allocation and the composition of the reserves being sold. An important policy implication is that if a portion of reserves has to be liquidated during a crisis, it may be optimal to sell assets with higher liquidation costs so as to remain with a more liquid portfolio in an environment of great market uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".