Nonparametric Market-Implied Greeks

We propose a novel, distribution-free framework for estimating option Greeks using machine learning. Unlike traditional parametric approaches, our method employs accumulated local effects (ALEs) to capture data-driven sensitivities without imposing functional form restrictions. Validation on synthetic Black–Scholes prices confirms the method’s ability to accurately recover theoretical Greeks. When applied to a comprehensive dataset of 14 million European S&P 500 options, we document notable empirical deviations: Vega sensitivity plateaus at high volatility levels, Gamma exhibits sharper peaks near at-the-money strikes, and Theta decay follows nonlinear patterns. These findings reveal important limitations of conventional models in capturing market-implied sensitivities. The model-agnostic framework, illustrated here with gradient-boosted trees, offers practitioners a robust, parametric-assumption-free alternative for risk measurement. Its versatility suggests potential for extension to hedging American options, exotic derivatives, and complex portfolios.