Bibliographic record
Abstract
Value at Risk (VaR) is one of the most commonly used ways to measure and monitor market risk. At JPMorgan Chase (JPM), very large derivative positions established by Bruno Iksil in the Synthetic Credit Portfolio (SCP) caused the bank’s Chief Investment Office (CIO) to exceed its VaR limit for four days in a row in January 2012. In response, the CIO changed to a new VaR model on January 30, which appeared to immediately reduce VaR by half. However, JPM soon discovered that this new VaR model had not been properly implemented and the bank went back to using the previous model. In addition, Iksil, other SCP staff, and their managers also disregarded several other risk metrics and limits during the first quarter of 2012. However, after JPM’s Chief Investment Officer learned on March 23 that Iksil and the SCP had breached the CIO’s mark-to-market Credit Spread Widening 10% risk limit the day before, she ordered trading of the SCP to be halted immediately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".