A Look at Model Uncertainty in the Evaluation of Commodity Contingent Claims: A Practitioner's Guide
Bibliographic record
Abstract
Model uncertainty in financial markets is prevalent by the very nature of how models are constructed and used by financial practitioners. As such, a proper characterization of model uncertainty should be paramount in the eyes of every practitioner, and furthermore, a proper framework for implementing such a characterization towards financial activities should be implicit. While model uncertainty is acknowledged by practitioners, a cohesive and robust framework for determining a model uncertainty risk measure that is broadly accepted by practitioners is missing. We acknowledge this deficiency and provide a practitioner's guide for evaluating a modern characterization of model uncertainty, specifically that of Li and Kwon, as applied to a subset of derivative related calculations, with the goal of promoting its implementation by practitioners. We promote its implementation by demonstrating the utility and flexibility of such a characterization relative to another modern model uncertainty risk measure, specifically that of Cont.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".