On Certain Goodness-of-Fit Tests in Operational Risk Modeling
Bibliographic record
Abstract
Measurement of operational risk, through a loss distribution approach (LDA), for the purpose of bank capitalization poses significant modeling challenges. As part of LDA, the severity of losses characterizing the mon- etary impact of potential operational risk events has to be modelled via a severity distribution. The selection of a best-fit severity distribution is essential for the accurate modeling of the impacts. In this article, we pro- vide an analysis of distributional properties of a family of goodness-of-fit tests suitable for more accurate selection of best-fit severity distributions. We describe first some classical results which are not known widely. We also demonstrate that certain goodness-of-fit test statistics popular in the financial industry do not have limiting distributions. For this reason, we provide a normalization that leads to a nondegenerate asymptotic distri- bution. Finally, a number of auxiliary results are presented that are of independent interest.
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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 | 0.068 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".