Closed‐form approximated pricing of multivariate derivatives under switching regime models
Bibliographic record
Abstract
Abstract Markov switching regime models have played an increasingly important role in finance and economics, especially for business cycles and long swings in currencies. Regime‐switching models provide a simple way to capture stochastic volatility and thus overcomes the drawback of the classical lognormality assumption characterized by constant volatility. This paper considers multivariate Black and Scholes type models with a Markov regime‐switching mechanism. We show that the pricing of some multivariate derivatives under models where the Markov chain has two or three states, can be approximated accurately in closed‐form, based on linear and quadratic Taylor polynomials. Closed form approximation methods are computationally advantageous as they perform in constant time, compared with alternative methods such as Monte‐Carlo, where the accuracy of the estimation is directly linked to the number of executed simulations.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".