An Efficient Convergent Willow Tree Method for American and Exotic Option Pricing under Stochastic Volatility Models
Bibliographic record
Abstract
Stochastic volatility models can describe the evolution of financial assets, such as stocks, currencies, and commodities, better than the classic Black–Scholes model. Some strategic decision-making problems also involve path-dependent and American-style options. In this article, the authors propose a novel, efficient, accurate, and unified two-factor willow tree method to price exotic and American options under the stochastic volatility models, such as the Heston, 3/2, 4/2, Hull–White, Stein–Stein, and a-Hypergeometric models. They also present the convergence analysis of their proposed tree method. They then apply the tree method to price European and American options, and the expected present value and survival rate in a dividend-and-ruin problem. Numerical results demonstrate the efficiency, accuracy, and convergence of their method. TOPICS:Options, volatility measures, factor-based models, analysis of individual factors/risk premia Key Findings • The authors propose an efficient and unified two-dimensional willow tree structure for various stochastic volatility models. • The convergence rate of the two-dimensional willow tree method is O(Δt). • The authors apply the willow tree to evaluate the present firm value and survival rate of a dividend-and-ruin problem, which embeds the lookback, the reflecting and absorbing barrier, and the stopping time features.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".