Ensemble deep neural network method for solving free boundary American style stochastic volatility models
Bibliographic record
Abstract
Abstract We present an ensemble deep learning method for solving free boundary American style stochastic volatility models. To this end, we cast our solution framework as a free boundary problem where the early exercise boundary surface, as a function of time and volatility, is approximated simultaneously with the value function and Greeks. For precise computation of the free boundary plane, we first use the Landau transformation to fix the free boundary and normalize the value function and the time domain. We then develop a novel ensemble auxiliary operator (EANO) involving suite of configurations based on the ensemble neural network output (ENNO). The early exercise boundary surface, value function, delta sensitivity, vega, gamma, vomma and vanna are predicted from the EENO, EANO, and the derivatives of EANO after training. The performance of our neural network configuration is verified and validated by comparison with some existing methods and examples. It provides an alternative approach for solving free boundary stochastic volatility 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".