Sequential Nested Simulation for Estimating Expected Shortfall
Why this work is in the frame
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Bibliographic record
Abstract
Expected shortfall (ES) is a widely used tail risk measure in the financial industry. Estimating the ES of a financial portfolio usually requires nested simulation, which is computationally burdensome. In a standard nested simulation procedure, one first simulates a set of plausible evolution of underlying risk factors, or the scenarios. Then, conditional on each outer scenarios, inner simulations are run to evaluate the financial positions in that scenario. In this work, we propose a sequential nested simulation procedure that dynamically allocates a fixed simulation budget to accurately estimate the expected shortfall. The goal is to gradually concentrate the simulation budget on the tail scenarios with the largest losses, as these scenarios are most relevant in ES estimation. Our numerical experiments show that, with the same simulation budget, the proposed procedure significantly improves the estimation accuracy of ES compared to a standard nested simulation procedure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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