Systemic Risk through Securitization: The Result of Deregulation and Regulatory Failure
Bibliographic record
Abstract
Without regulation, securitization allowed mortgage industry actors to gain fees and to put off risks. During the housing boom, the ability to pass off risk allowed lenders and securitizers to compete for market share by lowering their lending standards, which activated more borrowing. Lenders who did not join in the easing of lending standards were crowded out of the market. Artificially low risk premia caused the asset price of houses to go up, leading to an asset bubble and breeding fraud. The consequences of lax lending were thereby covered up. The market might have corrected this problem if investors had been able to express their negative views by short selling mortgage-backed securities, thereby allowing fundamental market value to be achieved. However, the one instrument that could have been used to short sell mortgage-backed securities - the credit default swap - was also infected with underpricing due to lack of minimum capital requirements and regulation to facilitate transparent pricing. As a result, there was no opportunity for short selling in the private-label securitization market. The authors propose countercyclical regulation to prevent a race to the bottom at the height of the business cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".