Did China Effectively Manage Its Foreign Exchange Reserves? Revisiting the Currency Composition Change
Bibliographic record
Abstract
To estimate the currency composition of China’s foreign exchange reserves and assess its effectiveness of management, the constrained least square method and variance sensitive analysis are utilized, respectively. Based on portfolio accounting identities, the change of foreign exchange reserves was decomposed into the net purchase change and the non-purchase change. The newly constructed non-purchase change was used to estimate the latent currency composition. Empirical results show that by the end of 2015Q1, China held about 63.6% of its reserves in the U.S. dollar, 19.6% in the euro, 3.09% in the Japanese yen, 4.89% in the pound sterling, 2.22% in the Canadian dollar, 2.03% in the Australian dollar, and 0.09% in the Swiss franc. Although the currency composition kept relatively stable, more attention had been paid to the emerging international currencies. China decreased the U.S. dollar share during the subprime crisis, while resorted to the portfolio rebalance strategy since 2011. The euro share and the pound sterling share declined during the European sovereign debt crisis. The first derivative of the U.S. dollar was positive while those of other currencies were negative before 2014Q3, and vice versa after 2014Q4. In general, the currency composition management of China’s foreign exchange reserves was effective.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".