Causal Regime-Aware Portfolio Allocation under Residual Stress and Tail-Risk Transmission

Abstract

Contemporary portfolio allocation faces fundamental limitations when confronted with non-stationary financial regimes and latent stress dynamics that elude traditional volatility-based risk measures. This paper develops a causal regime-aware allocation framework that integrates residual stress signals and tail-risk transmission channels to improve portfolio robustness under extreme market conditions. We argue that standard mean-variance optimization and its extensions fail to capture the structural persistence of drawdown regimes and the contagion mechanisms through which tail risks propagate across asset classes. By incorporating causal inference techniques to identify regime transitions and residual stress metrics that are leakage-safe against common volatility anomalies, the proposed architecture offers a systems-level reconfiguration of portfolio design. We examine the structural trade-offs between stability, adaptability, and computational tractability, emphasizing the role of governance frameworks and fairness constraints in deployment. The analysis draws on cross-domain comparisons from network epidemiology, critical infrastructure resilience, and macroeconomic stress testing to illustrate how residual stress signals can be operationalized within a causal graph of asset dependencies. Policy implications are discussed in the context of systemic risk regulation, transparency requirements, and the ethical use of predictive signals in automated investment systems. The paper concludes with forward-looking recommendations for integrating causal regime awareness into institutional portfolio governance, highlighting both the promise and the pitfalls of stress-aware allocation in the presence of model uncertainty and data sparsity.