Correlation: the most common myths in financial risk management practice

Financial institutions are required to hold capital reserves to protect against future losses, and many rely on mathematical models to estimate the necessary amounts. While these models can make risk measurement more precise, they are often used by practitioners who may not fully grasp their assumptions or limitations. One recurring source of error lies in the treatment of correlation—the statistical link between different risks—which plays a crucial role in determining how losses might combine. Misjudging these relationships has, in the past, amplified systemic vulnerabilities, most famously in the misuse of the Gaussian copula model during the subprime crisis. This paper revisits the problem of correlation mistaken beliefs from a pedagogical standpoint, using simplified examples and reproducible R code to show how small misunderstandings can have large consequences. The discussion aims to encourage greater critical awareness of quantitative tools among risk managers, regulators, and students alike.