Portfolio Risk Measurement via Stochastic Mesh with Average Weight
Why this work is in the frame
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Bibliographic record
Abstract
Nested simulation has been widely used in the risk measurement of derivative portfolio. The convergence rate of the mean squared error (MSE) of the standard nested simulation is <tex>$k^{-2/3}$</tex>, where <tex>$k$</tex> is the simulation budget. To speed the convergence, we propose a stochastic mesh approach with average weight to portfolio risk measurement under the nested setting. We establish the asymptotic properties of the stochastic mesh estimator for portfolio risk, including the bias, variance and then the MSE. In particular, we show that the MSE converges to zero at a rate of <tex>$k^{-1}$</tex>, which is the same as that under the non-nested setting. The proposed method also allows for path dependence of financial instruments in the portfolio. Numerical experiments show that the proposed method performs well.
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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 | 0.001 | 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 | 0.001 | 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) | 0.005 | 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