Extreme value techniques for stress scenario selection under elliptical symmetry and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The paper considers the problem of stress scenario selection, known as reverse stress testing, in the context of portfolios of financial assets. Stress scenarios are loosely defined as the most probable values of changes in risk factors for a given portfolio that lead to extreme portfolio losses. We extend the estimator of stress scenarios proposed in [P. Glasserman, C. Kang and W. Kang, Stress scenario selection by empirical likelihood, Quant. Finance 15 (2015), 1, 25–41] under elliptical symmetry to address the issue of data sparsity in the tail regions by incorporating extreme value techniques. The resulting estimator is shown to be consistent, asymptotically normally distributed and computationally efficient. The paper also proposes an alternative estimator that can be used when the joint distribution of risk factor changes is not elliptical but comes from the family of skew-elliptical distributions. We investigate the finite-sample performance of the two estimators in simulation studies and apply them on two financial portfolios.
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.
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 | 0.000 | 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 it