The increasing frequency and scope of financial crises have made global financial stability one of the major concerns of economic policy and decision makers. This has led to the understanding that financial and banking supervision has to be thought of as a systemic task, focusing on the interdependent relations among the institutions. Using network theory, we develop a dynamic model that uses a bipartite network of banks and their assets to analyze the system’s sensitivity to external shocks in individual asset classes and to evaluate the presence of features underlying the system that could lead to contagion. As a case study, we apply the model to stress test the Venezuelan banking system from 1998 to 2013. The introduced model was able to capture monthly changes in the structure of the system and the sensitivity of bank portfolios to different external shock scenarios and to identify systemic vulnerabilities and their time evolution. The model provides new tools for policy makers and supervision agencies to use for macroprudential dynamical stress testing.

We develop metrics to assess the complexity of a bank holding company (BHC), based on its ownership structure. Large BHCs have intricate ownership hierarchies involving hundreds or even thousands of legal entities that may contribute to increased operational risk and greater opacity. Our measures are mathematically grounded, intuitive, and easy to implement. They may be particularly useful in the context of resolution, where regulators often face significant time pressure and coordination challenges. We use regulatory filing data from the Federal Reserve to validate the measures, demonstrating that they provide a useful complement to balance sheet information in assessing BHC complexity. Notably, the proposed measures are highly correlated with existing complexity indicators that are not based on organizational structure and are less correlated with size than these existing complexity measures. We show that the proposed measures provide additional explanatory power for the regulatory indicators, even after controlling for size.