Bibliographic record
Abstract
Banks that use the advanced measurement approach to model operational risk may struggle to develop an internal process that produces stable regulatory capital over time. Large decreases in regulatory capital are scrutinized by regulators while large increases may force banks to set aside more assets than necessary. A major source of this instability arises from the loss severity selection process, especially when the selected distribution families for severity risk categories change year-to-year. In this report, we examine the process of selecting severity distributions from a candidate distribution list within the guidelines of the advanced measurement approach, propose useful tools to aid in selecting an appropriate severity distribution, and analyze the effect of selection criteria on regulatory capital. The log sinh-arcsinh distribution family is added to a list of common candidate severity distributions used by industry. This 4-parameter family solves issues introduced by the 4-parameter g-and-h distribution without sacrificing flexibility and shows promise in outperforming 2-parameter families, reducing the frequency of severity distribution families changing year-to-year. Distribution parameters are estimated using the maximum likelihood approach from loss data truncated at a known minimum reporting threshold. Our severity distribution selection process combines truncation probability estimates with Akaike Information Criterion (AIC), Bayesian Information Criterion, modified Anderson-Darling, QQ-plots, and predictive measures such as the quantile scoring function and out-of-sample AIC, and we discuss some of the challenges associated with this process. We then simulate operational losses and calculate regulatory capital, comparing the effect on regulatory capital of selecting loss severity distributions using AIC versus quantile score. A combination of these two criteria is recommended when selecting loss severity distributions.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".