INTERACTIONS BETWEEN THE EXCHANGE RATE OF RMB/USD IN THE ONSHORE AND OFFSHORE MARKETS: EVIDENCE FROM THE COMPARATIVE ANALYSIS ON ‘8.11’ EXCHANGE RATE REFORM IN CHINA
Bibliographic record
Abstract
This paper uses data pertaining to onshore and offshore markets before and after China’s ‘8.11’ exchange rate reform. The result verifies that after the exchange rate reform, the time series of RMB central parity rate (CPY), spot exchange rate and non-deliverable forward rate in Hongkong offshore market (CNH and NDF) have structural abrupt changes by Chow test from a quantitative perspective. On this basis, the co-integration test, Granger causality test, impulse response under the framework of VAR model, variance decomposition and other methods are used to further study the interaction between the above exchange rates before and after the exchange rate reform and make comparative analysis on them. The results show that the original formation mode of RMB central parity leads to long-term deviation between market transaction price and central parity, onshore price, and offshore price, thus affecting the market benchmark status and authority of the central parity rate. After the ‘8·11’ exchange rate reform in 2015, the trend of the central parity rate, the exchange rate in the onshore market and the offshore market converged significantly, the exchange rate difference reduced significantly, and the correlation between the onshore and offshore exchange rates strengthened.
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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 | 0.002 |
| 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 | 0.000 |
| 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".