Exact simulation of the 3/2 stochastic volatility model with stochastic jump intensity
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Bibliographic record
Abstract
This paper introduces a novel stochastic volatility (SV) framework that integrates jumps through a non-affine 3/2 SV structure and models jump intensity via a CIR-type stochastic process. Leveraging the measure change technique alongside the law of total expectation, we derive the moment generating function of the log-asset price process, facilitating the efficient pricing of both European options and VIX derivatives. For European option pricing, we implement a Hilbert interpolation method, which significantly improves computational efficiency and accuracy compared to conventional techniques. For VIX derivatives, we develop an exact simulation approach that reduces dimensional complexity without compromising precision. Numerical experiments confirm the computational efficiency and robustness of the proposed methods. Compared to standard Monte Carlo simulations, our approach delivers faster convergence and greater accuracy, establishing a flexible and effective modelling framework suitable for a wide range of quantitative finance applications.
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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 | 0.000 |
| 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 | 0.001 | 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