Real-world uncertainty rarely involves independent variables. Modeling correlations is essential for realistic risk analysis.

Understanding Correlation describes how variables move together—positively, negatively, or independently.

Modeling Approaches include Cholesky decomposition and copulas for complex dependencies.

Monte Carlo Simulation generates correlated scenarios for realistic output distributions.

Risk Implications are significant—correlated risks compound during stress events.