From Correlation to Causation: Bayesian Networks and Probabilistic Graphical Models for Stress-Aware Diversification in Practice

This article examines why correlation-based diversification often fails during market stress and argues that the limitation is conceptual rather than statistical. The author shows how Bayesian networks and probabilistic graphical models provide a causal framework for understanding regime-dependent dependence structures, shock propagation, and diversification breakdowns. By making economic drivers and transmission channels explicit, these models align formal analysis with practitioner intuition and offer a coherent foundation for stress testing, portfolio robustness assessment, and governance in non-stationary market environments.