Asian Options Pricing and Parameter Estimation of Uncertain Mean‐Reverting Currency Model With Exponential Ornstein–Uhlenbeck Exchange Rate
Bibliographic record
Abstract
This paper introduces an uncertain mean‐reverting currency model that incorporates floating domestic and foreign interest rates along with an exponential Ornstein–Uhlenbeck exchange rate process, all grounded in uncertainty theory. Pricing formulas for both Asian call and put options are derived within this framework. The parameters of the model are estimated using real financial data from Canada and the United States, including the Canadian Overnight Repo Rate Average (CORRA), the American Federal Funds Effective Rate (AFFER), and the monthly average exchange rate of the US Dollar to the Canadian Dollar (USDCAD). The method of moments is applied to estimate the unknown parameters, and goodness‐of‐fit tests are conducted to validate the parameter estimates. Numerical experiments demonstrate that Asian option prices decrease as domestic and foreign initial interest rates increase. The prices of call and put options show divergent behaviors with respect to the initial exchange rate and the fixed strike price. Additionally, the paper investigates the nonlinear relationship between option prices and expiration time. In the appendix, the uncertain currency model is transformed into a stochastic currency model, and statistical tests confirm its inapplicability to the selected data, thereby substantiating the choice of the uncertain currency model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".