Efficient Implementation of Tree-Based Option Pricing and Hedging Algorithms under GARCH Models
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Bibliographic record
Abstract
This article explores the use of lattice-based approximation schemes for pricing and hedging financial derivatives under GARCH models. The explosion problem and the computational cost associated with the implementation of GARCH-based trees have been well documented in the literature. To address these shortcomings, the authors propose a truncated mean-tracking tree that limits the number of nodes generated within the tree, focusing only on the relevant state space of the GARCH model. The authors assess the efficiency and accuracy of their approach by computing European style option prices and optimal quadratic hedges derived based on the local-risk minimization criteria under the physical measure. The authors test the effectiveness of their approach relative to the standard mean-tracking tree benchmark using different sets of GARCH parameters. Overall, the authors find that their truncation strategy significantly reduces the computational cost of implementing the tree, without sacrificing its accuracy, the largest gains being noticed for longer-term maturity contracts.
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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 | 0.000 | 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