The Impact of Financial Liquidity on the Exchange Rate of the Chinese Yuan Renminbi against the United States Dollar under Interest Rate Liberalization — Evidence from Shibor
Bibliographic record
Abstract
Financial liquidity is one of the most important factors that affects China’s currency market along with other macroeconomic factors. Shanghai Interbank Offered Rate (Shibor) is the quantitative indicator of the financial liquidity of China’s capital market. China has been continuously promoting the marketization of interest rates. The interbank market, as a resource of money supply, is significantly affected by this process. This paper empirically investigates the relationship between Shibor and the onshore renminbi-dollar exchange rate utilizing data from October 2006 to January 2015. Furthermore, in addition to Shibor, inflation rate difference, benchmark interest rate difference, GDP growth rate difference, and trade balance are all included as control variables. First, preliminary tests such as the ADF test and Granger causality test were conducted for selecting the most appropriate regression method. A VAR test, impulse response, and variance decomposition were then conducted. Empirical results show that Shibor has a significant impact on the onshore renminbi-dollar exchange rate, exhibiting a one-way causal relationship with an inverse direction. Furthermore, other control variables also show a different relationship to the onshore renminbi-dollar exchange rate to some extent. The finding is consistent with classical theory and practical judgment. This paper contributes to existing literature on classical theory testing of the exchange rate and provides new evidence on the influencing factors of the exchange rate.
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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 | 0.003 |
| 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".