A Fourier cosine expansion method for pricing FX-TARN under Lévy processes
Bibliographic record
Abstract
<abstract><p>In this paper, we extend the Fourier cosine expansion (COS) method to the pricing of {foreign exchange} target redemption note (FX-TARN), a popular exotic currency option. We take the FX spot rate and the cumulated positive cash flow as two state variables and factor the joint distribution by two marginals that can be approximated by Fourier cosine expansions. To recover the Fourier coefficients recursively, we approximate the two-dimensional integration by higher-order quadratures such as Gauss-Legendre or Clenshaw-Curtis quadrature for the integration over the spot rate. We derive the analytical formulas for the price under different knock-out types. We demonstrate that fast Fourier transform (FFT) can be employed to obtain the Fourier coefficients efficiently. We also evaluate the performance and accuracy of the method through a number of numerical experiments.</p></abstract>
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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 | 0.001 |
| 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".