The Design and Risk Management of Structured Finance Vehicles
Bibliographic record
Abstract
Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by short-term debt, thus bearing material rollover risk on their liabilities which led to defeasance. This paper models these vehicles, and shows that imposing leverage risk control triggers can be optimal for all capital providers, though they may not always be appropriate. The efficacy of these risk controls varies depending on anticipated asset volatility and fire-sale discounts on defeasance. Despite risk management controls, we show that a high failure rate is inherent in the design of these vehicles, and may be mitigated to some extent by including contingent capital provisions in the ex-ante covenants. Post the recent subprime financial crisis, we inform the creation of safer SIVs in structured finance, and propose avenues of mitigating risks faced by senior debt through deleveraging policies in the form of leverage risk controls and contingent capital.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 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".