Correlation Breakdown in the Valuation of Collateralized Fund Obligation
Bibliographic record
Abstract
In a collateralized fund obligation (CFO) several hedge funds are pooled in a fund that is in turn, securitized. Since the first securitization of a fund of hedge funds, launched in June 2002, these products haven9t had much impact among investors. This is not surprising considering that it is very difficult to predict the credit solvency of each tranche of the securitization and therefore its valuation. In this article a particular CFO is analyzed in an analytical pricing framework, capturing the correlation breakdown and the leptokurtic phenomena characteristics of hedge funds. The results show that due to a lack of transparency which is common in hedge funds, confidence intervals in probabilities of default and credit spreads, are high enough to burden CFO proliferation. <b>TOPICS:</b>Real assets/alternative investments/private equity, credit risk management, statistical methods
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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 | 0.001 | 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 | 0.000 |
| 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".