Generative flow networks improve diverse financial crisis scenario simulation
Discovery of Diverse and Realistic Financial Tail-Risk Using Generative Flow Networks
Machine Learning
Summary
Predicting rare but impactful financial events is difficult because available methods either focus too narrowly or explore inefficiently. The authors introduce GRID, a new way to generate many different and realistic potential financial crisis scenarios using a special kind of sampler called a Generative Flow Network. By sampling proportionally to risk instead of just maximizing it, GRID can better explore a wide range of severe event possibilities even when the risk estimates are uncertain. Tests on real financial crises show that GRID creates more diverse and realistic risky scenarios than other approaches.
What this means in practice
- •For financial risk teams: Generate a wide range of realistic financial crisis scenarios to improve risk assessment and planning.
- •For financial software developers: Integrate GRID’s flexible sampling method into risk simulation tools to produce diverse tail-risk scenarios even with imperfect risk models.$Commercial implications: Enables the development of advanced, market-ready financial risk management software offering more comprehensive scenario coverage.
Authors
Adir Serruya, Gilad Katz
Abstract
Accurate modeling of rare, high-impact financial events is essential for robust risk management. Sampling-based techniques such as Sequential Markov Chain Monte Carlo (SMCMC) generate diverse scenarios but inefficiently explore high-risk regions, while Deep Reinforcement Learning (DRL) converges on narrow sets of scenarios, limiting diversity. We present GRID, which uses Generative Flow Networks (GFlowNets) -- samplers that draw objects in proportion to a reward -- to systematically generate diverse and realistic financial tail-risk scenarios. Building on continuous GFlowNets, GRID introduces a flexible mixture-policy architecture with variable-specific distribution families for high-dimensional, sequential macro-financial data, together with plausibility constraints that keep generated trajectories realistic. A central practical advantage is robustness to reward-proxy imperfection: because GFlowNets sample proportionally rather than maximize, they remain broadly exploratory even when the oracle's out-of-sample accuracy is low -- an unavoidable reality in financial forecasting. On three real-world crises, GRID improves scenario diversity, realism, and tail-risk coverage over RL and MCMC baselines, and ablations over oracle architectures and distribution families yield practical insights into continuous GFlowNet behavior under noisy rewards.