Bibliographic record
Abstract
model and the back testing procedure are important parts of the banks market risk framework. The Value-at-Risk model provides a daily measure th exceed once every 100 days. DNB and Rabobank agreed to perform a periodic analysis of the VaR model that goes beyond the regulatory guidelines. Every quarter Rabobank International tests the accuracy of its VaR model using the regulatory back test. This back test checks the number of times the VaR was breached (called exception). Based on this number of exceptions this test judges if the VaR model is accurate or not. The regulatory back test has its limitations. Therefore, we conducted a literature research to investigate alternative back test methods. This resulted in a framework of five back tests that together test the most important properties of a VaR model: - exception frequency: the number of realised exceptions - exception clustering: independency of exceptions over the tested period. - exception size: the size of the exception We implemented the five back tests in a test framework that Rabobank International can use for the periodic back testing beyond regulation. >
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".