Statistical Evaluation of Value at Risk Models for Estimating Agricultural Risk
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops a skewness and leptokurtic modified VaR model with a mixture weight parameter that blends the Cornish-Fisher and EWMA methods. We estimate and evaluate five existing parametric VaR specifications using weekly returns for Canadian feedlot cattle feeding margin data and Maple Leaf Foods stock return data. The estimation of VaR based on EWMA method yields the most satisfactory results particularly for returns with positive skewness or leptokurtic tails. Meanwhile, the VaR forecasts obtained using the Cornish-Fisher method provides a relatively better tracking of the observed returns compared to the other methods, and therefore, has lower forecast error. Our proposed model allows users to determine the value of VaR based on their own risk preferences.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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