The Risk of Tranches Created from Mortgages
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Bibliographic record
Abstract
Using the criteria of the rating agencies, the authors tested how wide the AAA tranches created from residential mortgages can be. They found that the AAA ratings assigned to ABSs were not totally unreasonable but that the AAA ratings assigned to tranches of Mezz ABS CDOs cannot be justified.We examined the AAA ratings that were assigned to the structured products created from residential mortgages between 2000 and 2007. We considered both asset-backed securities (ABSs), which are created from a pool of mortgages, and ABS collateralized debt obligations (ABS CDOs), which are created from a portfolio of BBB rated ABS tranches. Standard & Poor’s and Fitch Ratings used probability of loss as the basis for rating tranches; Moody’s Investors Service used expected loss. We considered both criteria and tested how wide they allowed AAA tranches to be in different circumstances. In addition to the widely used Gaussian copula constant recovery rate model, we considered models whose recovery rate decreases as the default rate increases and models whose defaults are driven by a non-Gaussian copula model that increases the probability of extreme outcomes. When considering ABS CDOs, we used a two-factor model that distinguishes between within-pool default correlation and between-pool default correlation.We found that the AAA ratings assigned to senior ABS tranches were not totally unreasonable. For many of the assumptions that rating agencies might reasonably have made, expected loss and probability of loss were not markedly different from those of AAA rated bonds whose lives equaled the expected lives of the tranches. But the AAA ratings assigned to tranches of ABS CDOs cannot be justified. The risk of an ABS CDO tranche depends critically on the correlation between mortgage pools and the correlation model. A key point is that BBB tranches of ABSs cannot be considered equivalent to BBB bonds for purposes of subsequent securitizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it