GAN-Enhanced Implied Volatility Surface Reconstruction for Option Pricing Error Mitigation

The accurate modeling of implied volatility surfaces is crucial for option pricing and risk management in financial markets. Traditional parametric approaches, such as the Stochastic Volatility Inspired (SVI) model, often suffer from rigid functional forms that inadequately capture the complex nonlinear patterns observed in real market data, particularly in the tail regions of volatility smiles. This paper introduces a novel Generative Adversarial Network (GAN) framework specifically designed to learn and reconstruct implied volatility surfaces directly from historical option market prices. Our approach leverages the adversarial training mechanism to generate smooth, market-consistent volatility surfaces that overcome the limitations of conventional parametric models. The proposed GAN architecture incorporates domain-specific constraints and regularization techniques to ensure the generated surfaces satisfy fundamental no-arbitrage conditions while maintaining computational efficiency. Through comprehensive empirical evaluation using real-world AAPL options data spanning 2016-2020, we demonstrate that our GAN-based approach significantly outperforms traditional methods in reducing pricing errors under the Black-Scholes framework. Specifically, our method achieves a 23.7% reduction in mean absolute percentage error (MAPE) for out-of-the-money options and a 31.4% improvement in root mean square error (RMSE) compared to the baseline SVI model. The results indicate particularly substantial improvements in pricing accuracy for deep out-of-the-money options, where traditional models typically exhibit the largest deviations. Furthermore, our approach demonstrates superior stability across different market regimes and provides enhanced generalization capabilities for options with varying maturities and moneyness levels. The proposed methodology offers significant practical implications for derivatives trading, risk management, and model validation in quantitative finance.