The shifted GARCH model with affine variance: Applications in pricing
Bibliographic record
Abstract
This paper introduces a modification to the affine GARCH model of Heston and Nandi (2000). The new model is designed to allow for a non-zero lower bound for the variance achieved by adding two parameters to the existing model. The affine structure of the moment-generating function is preserved at the level of variance, while an approximation is studied for log prices. The construction resembles the shifted continuous-time Heston (1993) model. Maximum likelihood estimation is performed on real data, and the model is shown to improve the fitting of the implied volatility surface, particularly for deep out-of-the-money options. • We introduce a shifted affine GARCH model with a non-zero lower bound on variance. • The model fits US indices significantly better than the standard model. • The model allows for closed form (approximative) pricing of derivatives. • The fit to deep out-of-the-money put options is significantly improved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".