Scenario Generation and Stress Testing for Cryptocurrency Markets using GAN and Diffusion-Based Generative Models

In this research, we propose a novel hybrid generative framework that combines diffusion models and generative adversarial networks (GANs) to generate realistic financial market scenarios under both normal and extreme conditions. We apply our model across the Layer 1 cryptocurrency blockchain, such as Bitcoin (BTC-USD), Ethereum (ETH-USD), Ripple (XRP-USD), and Litecoin (LTC-USD), in a context characterized by data scarcity and high volatility. By conditioning on macroeconomic covariates and global indices, our model captures both structural and cyclical dynamics, allowing for effective stress testing and robust forecasting. We compare our approach with traditional time series models and classical simulation techniques, and integrate SHAP-based explainability to assess the economic plausibility of generated scenarios. Our framework demonstrates the potential to enhance decision making under uncertainty in financial risk management, portfolio allocation, and regulatory stress testing. This study bridges the gap between modern generative AI techniques and real-world financial constraints in emerging markets.