Diffusion Models for Synthetic Financial Stress Scenario Generation and Robust Risk Management Evaluation
Keywords:
diffusion models, financial stress testing, synthetic scenario generation, robust risk management, generative artificial intelligence, systemic risk, model governance, regulatory technologyAbstract
Financial stress testing and scenario generation are critical components of systemic risk management, yet traditional methods often rely on historical data that cannot capture unprecedented crisis dynamics. This paper presents a comprehensive framework for leveraging diffusion probabilistic models to generate synthetic financial stress scenarios that are both plausible and diverse, enabling robust evaluation of risk management strategies. We examine the architectural considerations of adapting diffusion models for multivariate financial time series, including the design of forward and reverse processes over temporal sequences, the incorporation of economic regime constraints, and the calibration of noise schedules to preserve inter-asset correlations and tail dependencies. The system-level trade-offs between scenario realism and computational tractability are analyzed in the context of large-scale portfolio simulations and regulatory stress tests. We further discuss the governance implications of using synthetic data for capital adequacy assessments, including concerns about model validation, fairness across market participants, and potential misuse for regulatory arbitrage. The paper evaluates the robustness of diffusion-based generation under distribution shift and adversarial perturbations, drawing comparisons with generative adversarial networks and variational autoencoders. We propose a set of infrastructure design principles that prioritize transparency, reproducibility, and auditability for deployment in central bank and supervisory environments. By integrating insights from econometrics, machine learning, and socio-technical systems, this work provides a roadmap for the responsible adoption of diffusion models in financial stability monitoring. The findings underscore the need for continuous human oversight and adaptive regulatory frameworks to harness the benefits of generative artificial intelligence without amplifying systemic fragility.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
