Bibliographic record
Abstract
Primbs et al. (2007) proposed an option pricing method using a pentanomial lattice that incorporated mean, volatility, skewness and kurtosis. This approach is very useful when the return of the underlying asset follows a lognormal distribution. However, Primbs et al. (2007) claimed that "with four moments, one could conceivably use a quadrinomial lattice (i.e., four branches); however, the recombination conditions along with the requirement of non-negative probabilities are quite limiting in terms of the range of skewness and kurtosis that can be captured". In this research, as a refutation, a quadrinomial lattice model has been developed incorporating mean, volatility, skewness, and kurtosis; and it has been shown that the conditions for the non-negative probabilities are the same as the conditions obtained for the pentanomial lattice in Primbs et al. (2007). Several numerical examples are presented to compare the result obtained from the quadrinomial lattice with that of the pentanomial lattice.
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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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".