An Empirical Analysis of Volatility Characteristics of Inter-Bank Offered Rate of International Financial Centers
Bibliographic record
Abstract
This paper compares the trails of volatility of the inter-bank offered rate of five international financial centers with that of Shanghai Inter-Bank Offered Rate (Shibor), using VaR-GARCH model. Considering the influences of local lending rate system and economic environment, it also provides policy implications to improve the pricing approach of Shibor and the revolution of marketization of interest rate, and to increase the reference value of Shibor. Previous research usually analyzes the volatility of the lending rate of one or two markets with the same currency. However, this paper compares Shibor with lending rates of five different global financial centers, which helps correct Shibor’s pricing system and the market it involves. Moreover, this paper uses various kinds of models from the GARCH family to find the optimal one for each market, instead of modelling all different rates with the same model. By doing so it can obtain the model which matches each market best and increase the accuracy of the results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".