Estimation of multifactor stochastic volatility jump-diffusion models: A marginalized filter approach
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Bibliographic record
Abstract
A new deterministic filtering and estimation methodology is developed for a broad class of affine multifactor stochastic volatility jump-diffusion models. This filtering approach leverages a marginalized discrete nonlinear filter that differentiates between jump components and Gaussian noises . By conditioning on the jump components, the procedure employs Kalman-like recursions to manage Gaussian noises and states that appear linearly in the model’s dynamics, while jumps are addressed by constructing a grid of likely values. The efficacy of this methodology is demonstrated through extensive simulation studies. Additionally, the practical application is highlighted by implementing the approach on risk-neutral cumulants derived from European S&P 500 option prices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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