Hybrid Generative Adversarial Network (GAN) Framework for Vanilla Option Pricing: Evidence from the NIFTY 50

Abstract

This study presents a hybrid modeling framework that integrates Generative Adversarial Networks (GANs) with Monte Carlo simulation to improve the pricing of vanilla options on the NIFTY 50 index. Traditional pricing models often rely on parametric assumptions that may not fully capture the nonlinear dependencies, volatility clustering, and regime shifts characteristic of emerging markets. GANs, commonly known for generating images in AI applications, offer a data-driven alternative by learning latent structures from historical market data and generating synthetic asset paths that reflect realistic statistical properties. These high-fidelity trajectories are then used as inputs to a Monte Carlo pricing engine for standard European- style call options. Empirical results demonstrate that the GAN-based approach yields comparable pricing accuracy to conventional methods, with occasional outperformance in distributional realism — albeit at the cost of significantly higher training time due to its iterative and data-intensive nature. This hybrid methodology bridges deep learning and financial engineering, offering a scalable and adaptive solution for derivative pricing in data-rich environments. The framework also holds promise for broader applications in risk management, portfolio stress testing, and market scenario analysis.