A study of prepayment risks in China's mortgage-backed securitization
Bibliographic record
Abstract
Prepayment decisions bring risks to the process of mortgage-backed securitization (MBS) and make pricing of these assets difficult. With data from a Chinese bank's mortgage-backed security pool, we examine prepayment decisions of the borrowers. Since multiple prepayments are allowed without penalty in China, we also distinguish between the probability and the proportion of prepayment. With discrete choice models and a Type II Tobit model, we conclude that the probability of prepayment is affected by the starting account balance, monthly effects, duration, term of debt, house type, trading type of loan, house value and size of mortgage. The probability is also affected by individual factors, including gender, age, education level, individual income and family income. Interestingly, the female borrowers are more likely to prepay, while older borrowers intend to prepay a fewer number of times but with a larger amount each time. In addition, similar factors as above are also shown to explain the choices of proportions of prepayment, with some reasonable adjustments. Our results imply that the variations in income, expenditure, geographic difference and opportunities in the financial market should be included for consideration of risk in the MBS process in China.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".