A Study on Prepayment Risk of Residential Mortgage-backed Securitization in China—A Case of "JIAMEI 2018-1 MBS Program"
Bibliographic record
Abstract
The Residential Mortgage-Backed Securities market (RMBS) has played a large role in enhancing the liquidity of mortgages, and in relieving the pressure of capital constraints on commercial banks. There are three main risks associated with residential mortgage-backed securitization: prepayment risk, interest rate risk, and default risk. This paper analyzes the prepayment risk in the "JIAMEI 2018-1"RMBS, the largest single RMBS product of the Postal Saving Bank of China (About 14 billion RMB). We introduce the JIAMEI 2 018-1 and study the factors that affect its prepayment risk. Using data, we conclude that the products involved in this case have comparatively low prepayment risk, even under the double influence of the housing finance policy tightening and the impact of the COVID-19 Pandemic.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".