  Accurately defining, measuring and mitigating risk is a cornerstone of
financial risk management, especially in the presence of financial contagion.
Traditional correlation-based risk assessment methods often struggle under
volatile market conditions, particularly in the face of external shocks,
highlighting the need for a more robust and invariant predictive approach.
  This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO
VaR), a novel methodology that significantly advances causal inference in
financial risk analysis. Embracing a causal network framework, this method
adeptly captures and analyses volatility and spillover effects, effectively
setting it apart from conventional contagion-based VaR models. Causal-NECO
VaR's key innovation lies in its ability to derive directional influences among
assets from observational data, thereby offering robust risk predictions that
remain invariant to market shocks and systemic changes.
  A comprehensive simulation study and the application to the Forex market show
the robustness of the method. Causal-NECO VaR not only demonstrates predictive
accuracy, but also maintains its reliability in unstable financial
environments, offering clearer risk assessments even amidst unforeseen market
disturbances. This research makes a significant contribution to the field of
risk management and financial stability, presenting a causal approach to the
computation of VaR. It emphasises the model's superior resilience and invariant
predictive power, essential for navigating the complexities of today's
ever-evolving financial markets.
