Markov-Modulated and Shifted Wishart Processes with Applications in Derivatives Pricing
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Bibliographic record
Abstract
The popular Wishart (WI) processes, first introduced by Bru in 1991, exhibit convenient analytical properties for modeling asset prices, particularly a closed-form characteristic function, and the ability to jointly model stochastic volatility and correlation. These features tend to increase substantially during crisis periods, more than predicted by a Wishart dynamic. Moreover, the variance processes implied by the Wishart, similar to CIR models, have no buffer away from zero. In this paper, we introduced the Markov-Modulated Shifted Wishart processes (MMSW) and the embedded Shifted Wishart processes (SW) to address these shortcomings in the modeling of asset prices. We obtain analytical representations for several characteristic functions. We also estimate the parameters and evaluate the price of Spread options via the Fourier transform under the two new models compared to the standard Wishart. Our analyses demonstrate a significant impact of the MMSW process compared to the standard Wishart process of up to 7% in Spread 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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