Valuation of Exotic Options and Counterparty Games Based on Conditional Diffusion

This paper addresses the challenges of pricing exotic options and structured products, which traditional models often fail to handle due to their inability to capture real-world market phenomena like fat-tailed distributions and volatility clustering. We introduce a Diffusion-Conditional Probability Model (DDPM) to generate more realistic price paths. Our method incorporates a composite loss function with financial-specific features, and we propose a P-Q dynamic game framework for evaluating the model's economic value through adversarial backtesting. Static validation shows our P-model effectively matches market mean and volatility. In dynamic games, it demonstrates significantly higher profitability than a traditional Monte Carlo-based model for European and Asian options. However, the model shows limitations in pricing products highly sensitive to extreme events, such as snowballs and accumulators, because it tends to underestimate tail risks. The study concludes that diffusion models hold significant potential for enhancing pricing accuracy, though further research is needed to improve their ability to model extreme market risks.