The Impact of Default Correlations on the Prices of Collateralized Bond Obligat
Bibliographic record
Abstract
This paper presents a numerical procedure for pricing collateralized bond obligations (CBO) and analyze the impact of default correlations for the prices of collateralized bond obligations. Specifically, we adopt default correlation model of Zhou (2001) and first passage time model of Black and Cox (1976). The model of Black and Cox is used for estimating the value of the firm and the volatility of the firm value which are unobservable variables. We find that the impact of default correlations on the prices of collateralized bond obligations is generally quite large. This can be tested by carrying out Monte-Carlo simulations for firm value processes, assuming first no default correlations and second modeling default correlations between the processes. We also compare the model prices and recently issued CBO market price and find that no default correlation model over prices the issued CBO and default correlation model under prices the issued CBO. These results in this paper emphasize that modeling default correlations is very important in analyzing CBO and a more complicated further analysis is required.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".