Option Implied Volatility and Trading Strategies Based on Neural Network Correction

We extend classical option‐pricing models by adding a neural network correction that captures the intricate curvature of the implied volatility (IV) surface, even in highly nonlinear regions. Using daily SSE 50 ETF option data, we propose a two‐stage hybrid framework that first fits a parametric model and then trains a feedforward neural network to correct residual errors. The correction is updated in a rolling out‐of‐sample procedure and significantly improves IV predictions across multiple horizons. To assess the trading performance of these predictions, we implement a delta‐neutral volatility trading strategy. The hybrid approach outperforms benchmark models in both predictive accuracy and trading performance, delivering higher Sharpe ratios and superior risk‐adjusted returns. Our results provide new empirical evidence from the Chinese derivatives market and demonstrate that theory‐guided machine learning is especially useful for improving the accuracy and applicability of option pricing models.