What about Underevaluating Operational Value at Risk in the Banking Sector
Bibliographic record
Abstract
The objective of this article is to develop a precise and rigorous measurement of a bank's operational VaR. We compare our model to the standard model frequency used in practice. This standard model is constructed based on lognormal and Poisson distributions which do not take into account any data which fall below the truncature threshold and undervalue bank's exposure to risk. Our risk measurement also brings into account external operational losses that have been scaled to the studied bank. This, in effect, allows us to account for certain possible extreme losses which have not yet occurred. The GB2 proves to be a good candidate for consideration when determining the severity distribution of operational losses. As the GB2 has already been applied recently in several financial domains, this article argues in favor of the relevance of its application in modeling operational risk. For the tails of the distributions, we have chosen the Pareto distribution. We have also shown that the Poisson model, unlike the negative-binomial model, is retained in none of the cases for frequencies. Finally, we show that the operational VaR is largely underestimated when the calculations are based solely on internal data.
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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 | 0.026 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".