Not all VIXs are (Informationally) equal: Evidence from affine GARCH option pricing models
Bibliographic record
Abstract
This paper examines which VIX maturity to use in affine GARCH model estimation, when the objective is to do option pricing. Utilizing the Model Confidence Set approach repeatedly, we rank the best VIXs across different dynamic models. Our results highlight the importance of estimating with VIXs and show that with the appropriate VIX a reduction of up to 38% in option pricing errors can be obtained. Our results also show that the 1-year VIX is the worst to use, that the 1-month VIX is an overall favourite, and that the choice of VIX maturity matters mostly for more flexible models. • We examine which VIX maturity to use in affine GARCH model estimation. • Our results highlight the importance of estimating with VIXs. • With the appropriate VIX a reduction of up to 38% in option pricing errors can be obtained. • The 1-year VIX is the worst to use and the 1-month VIX is an overall favourite. • The choice of VIX maturity matters mostly for more flexible models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".