Local Estimation for Option Pricing: Improving Forecasts with Market State Information

We propose a novel estimation framework for option pricing models that incorporates local, state-dependent information to improve out-of-sample forecasting performance. Rather than modifying the underlying option pricing model, such as the Heston-Nandi GARCH or the Heston stochastic volatility framework, we introduce a local M-estimation approach that conditions on key state variables including VIX, realized volatility, and time. Our method reweights historical observations based on their relevance to current market conditions, using kernel functions with bandwidths selected via a validation procedure. This adaptive estimation improves the model’s responsiveness to evolving dynamics while maintaining tractability. Empirically, we show that local estimators substantially outperform traditional non-local approaches in forecasting near-term option implied volatilities. The improvements are particularly pronounced in low-volatility environments and across the cross-section of options. The local estimators also outperform the non-local estimators in explaining future option returns. Our findings suggest that local information, when properly incorporated into the estimation process, can enhance the accuracy and robustness of option pricing models.