Causal Discovery of Volatility Spillovers in Global Markets via Interpretable Machine Learning with Walk-Forward Robustness Guarantees
Keywords:
causal discovery, volatility spillovers, interpretable machine learning, walk-forward validation, systemic risk, financial infrastructure, robustness guaranteesAbstract
Understanding the propagation of financial volatility across global markets is a fundamental challenge for systemic risk assessment, portfolio construction, and regulatory oversight. Traditional econometric approaches, such as Granger causality tests and vector autoregressive models, impose strong linearity and stationarity assumptions that fail to capture the nonlinear, time-varying nature of modern financial interconnectedness. This paper introduces a framework for causal discovery of volatility spillovers that integrates interpretable machine learning with rigorous walk-forward robustness guarantees. We develop a system architecture that combines constraint-based causal structure learning with attention-based neural network components, enabling the extraction of directed acyclic graphs representing volatility transmission channels. The walk-forward validation regime ensures that all causal estimates are generated using only information available at the time of inference, thereby avoiding look-ahead bias and producing out-of-sample stability guarantees. We discuss the structural trade-offs between model complexity, interpretability, and computational feasibility in large-scale deployment across multiple asset classes and geographies. The paper further examines governance implications, including the need for algorithmic transparency in systemic risk monitoring, fairness considerations in cross-border spillover estimation, and policy recommendations for regulatory adoption of interpretable causal models. Our analysis highlights the importance of combining causal discovery with robustness guarantees to build trustworthy decision-support infrastructures for global financial stability.
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