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 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 | 0.011 | 0.004 |
| 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 | 0.001 | 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".