Forecasting implied volatility surface with generative diffusion models
A conditional Denoising Diffusion Probabilistic Model (DDPM) was developed to generate one-day-ahead implied volatility surfaces that are approximately arbitrage-free. The model conditions its generation on historical market variables and uses a dynamically weighted loss function based on the signal-to-noise ratio to penalize arbitrage violations during training, outperforming existing models.