Bibliographic record
Abstract
This paper reviews the use of Fourier transform methods in the pricing of contingent claims. This is a very promosing topic in finance, given the scarcity of closed-form solutions for derivative prices. It is shown that solving for the Fourier Transform is much easier than solving for the price, especially so under complex probability models, such as affine jump diffusions. In fact, explicit solutions for the Fourier transform are guaranteed for many types of contingent claims. As a consequence, the only remaining numerical issue in that case is the inversion of the Fourier transform. Resume Dans ce papier nous passons en revue la methode de transformee de Fourier tels qu’elle est utilisee pour l’evaluation des biens contingents. Cette voie de recherche est encore prometteuse etant donnee la rarete des formes explicites pour les prix de certains produits derives. En fait, pour les modeles probabilistes riches comme les processus affines avec sauts, il est plus facile de resoudre pour la transformee de Fourier du prix que le prix lui meme. On constate a cet effet que plusieurs biens contingents permettent d’avoir la transformee de Fourier sous une forme explicite et pour trouver le prix, il suffit de l’inverser, tres souvent numeriquement. Les Cahiers du GERAD G–2010–72 1
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".