Stress Testing with Generative Scenario Models Using Diffusion-Based Market Simulators

Abstract

The stability of global financial systems relies heavily on the ability of institutions to anticipate and withstand extreme market deviations. Traditional stress testing methodologies, primarily predicated on historical simulation or parametric Monte Carlo methods, often fail to capture the complex, non-linear dependencies and fat-tailed distributions inherent in financial time series during black swan events. This paper introduces a novel framework for financial stress testing utilizing diffusion-based generative models. We propose a conditional diffusion probabilistic model adapted for temporal data, capable of generating high-fidelity synthetic market trajectories that adhere to user-defined stress conditions. Unlike Generative Adversarial Networks (GANs), which suffer from mode collapse and training instability, our diffusion-based approach ensures diverse scenario generation by iteratively denoising random Gaussian processes under guided constraints. We evaluate our model against standard baselines using S&P 500 and volatility index data. The results demonstrate that the proposed architecture not only reproduces the statistical properties of historical data with higher accuracy but also generates plausible, severe stress scenarios that exceed historical precedents in terms of severity and structural coherence. This research bridges the gap between state-of-the-art computer vision generative techniques and quantitative risk management, offering a robust tool for systemic risk assessment.