Navigating the complexities of option pricing! This paper presents techniques for pricing portfolios with American options under worst-case scenarios, acknowledging the uncertainty in diffusion parameters. The analysis requires an algorithm that takes into account the exposure to risk changes when non-vanilla components are knocked out or exercised early. Researchers introduce heuristics to reduce computational complexity from early exercise combinations, leading to nearly half the compute time. In this case the sell-side would have a nonlinear algorithm that maximizes the expected liability under the risk-neutral measure. [Formula: see text] depends on the portfolio under consideration. The algorithms and heuristics that result from this research could lead to more efficient computation of option prices. Moreover, the insights could help improve risk management. All this leads to better financial security and safety from market fluctuations.
Published in the International Journal of Theoretical and Applied Finance, this research aligns with the journal's focus on financial modeling and derivatives pricing. By addressing the complexities of pricing American options under uncertain volatility, the paper fits well within the journal's scope.