Financial finance valuation applied to option trading

Abstract A framework is developed for option positioning and trading based on Financial Finance Valuation (FFV), a methodology that defines value as the infimum of expected future cash flows under different test measures plus a rebate for the less likely outcomes. Positions are designed by solving a max-min optimization problem where the inner minimization evaluates a given position and the outer maximization selects the most valuable configuration. The optimization problem is solved efficiently using Disciplined Saddle Programming (DSP). The framework is implemented assuming a base measure in which return dynamics follow a Sato process with bilateral gamma laws at unit time. Backtests on SPY, AAPL and AMZN options from January 2017 to December 2024 show consistent compounding returns and stable risk-adjusted performance. The results demonstrate that FFV-based positioning can generate practical, implementable option trading strategies that adapt to changing market conditions while maintaining rigorous risk controls.