Analysis of Premium Determinants and Pricing Models for Chooser Options Using Binomial Tree, Parity Decomposition, and Monte Carlo Methods
Bibliographic record
Abstract
Chooser options are exotic financial derivatives allowing investors to select be-tween a call and a put option on the same underlying asset at a predefined future date, ad-dressing the limitation that most exotic options require upfront payoff structure determination and making them valuable for investors uncertain about market trends. This study prices Eu-ropean and American chooser options using three core methods: the multi-step binomial tree model, the parity decomposition method and the Monte Carlo method. It compares the bino-mial tree and Monte Carlo methods, finding they converge to similar results with sufficient steps or simulations, while the parity decomposition method aligns with the binomial tree model. Key factors affecting chooser option premiums are clarified: strike price near the as-set’s initial price maximizes flexibility with premiums between single options and straddles, extreme strike prices make its value converge to a put or call option, higher volatility ac-celerates premium growth, European choosers benefit from longer observation periods and American ones from early exercise. This study enriches exotic option pricing literature and aids investors/financial institutions in uncertain markets.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".