An Empirical Study about Influence of China’s Shadow Banking on the Stability of the Financial System
Bibliographic record
Abstract
With the rapid development of the financial system in recent years, all kinds of financial derivatives teem and the size of the shadow banking is becoming more and bigger. It has become an important factor affecting the stability of China’s financial system. The influence of shadow banks on the financial system has two sides, on the one hand it is advantageous to the development and expansion of small and medium-sized enterprises as lubricant of corporate financing, on the other hand, features of shadow banking that highly leveraged and term mismatch also bring uncertainty to China’s financial system. Firstly, this paper calculates the size of the shadow banking in China, and then builds a fuzzy comprehensive evaluation system to evaluate the risk of China’s financial system. When determining the evaluation index, this paper apply KMV model to calculate the credit risk of China’s securities market, and the maximum entropy method to determine the index weight. After getting China’s financial system risk index and the size of shadow banking, this paper constructs the VAR model and makes the parameter estimation and impulse response analysis. Analysis results show that in a certain degree, the increase of the scale of shadow banks can reduce the risk of the financial system, but if it is over some certain threshold, it will increase the overall risk of the financial system.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".