Graph Neural Reinforcement Learning for Cross-Market Contagion-Aware Portfolio Risk Control Under Extreme Financial Stress
Keywords:
Graph neural networks; reinforcement learning; portfolio risk management; financial contagion; extreme market stress; cross-market dependencies; systemic risk; interpretabilityAbstract
The intensification of cross-market financial linkages during periods of extreme stress has exposed fundamental limitations in conventional portfolio risk control frameworks, which typically treat markets as isolated entities and rely on linear correlation structures that collapse under crisis dynamics. This paper proposes a graph neural reinforcement learning (GNRL) architecture that directly encodes the latent network topology of global financial markets into the decision process of an adaptive portfolio agent, enabling it to internalize contagion pathways and adjust exposures before systemic propagation crystallizes. The system constructs a dynamic graph with nodes representing individual assets or market indices and weighted edges capturing time-varying dependencies such as volatility spillovers, tail dependencies, and information flow. A graph neural network encoder processes this evolving structure to generate state representations that preserve higher-order contagion signatures. These representations feed a policy network trained via actor-critic reinforcement learning, where the reward function integrates risk-adjusted returns with drawdown penalties and stress-sensitive adjustments derived from composite stress indices. The architecture is examined not only through its algorithmic design but also through the lens of system-level tradeoffs, including latency constraints in live trading pipelines, resilience to concept drift during regime shifts, and the governance challenges posed by autonomous rebalancing in illiquid environments. We analyze the implications of deploying such a system at scale, focusing on fairness across market participants, the potential for unintended amplification of systemic risk due to homogeneity in learned policies, and the interpretability requirements demanded by prudential regulators. Infrastructure considerations such as cloud-native orchestration, continuous model validation under realistic walk-forward protocols, and the incorporation of leakage-safe early warning signals are discussed in depth. By synthesizing advances in graph representation learning, deep reinforcement learning, and financial stress analytics, this study provides a comprehensive blueprint for building next-generation portfolio control systems that are both contagion-aware and operationally viable under the most severe market conditions.
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