Estimation of correlations in portfolio credit risk models based on noisy security prices
Bibliographic record
Abstract
Portfolio credit risk models are very often constructed with correlation matrices serving as proxies for interrelations in the creditworthiness of each company. In addition to the size of the matrix, estimation of correlation is also complicated by the fact that defaults are rare and credit-sensitive securities such as stocks, bonds and credit default swaps (CDS) are noisy. Therefore, we present in this paper an estimation approach based on credit-sensitive instruments that accounts for noise and is highly parallelizable, the latter being a very important feature for large portfolios in finance. A simulation study shows that the method is reliable and has better statistical properties when benchmarked against other correlation estimators. In an empirical study based on the CDS premiums and stock prices of 225 firms listed on the CDX North American indices, we analyze the correlations computed using numerous approaches. Overall, we find that ignoring noise severely underestimates correlations, whereas equity correlation is poorly related to the best correlation estimates inferred from the CDS market.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".