Critical Regimes of Systemic Risk: Flow Network Cascades in the U.S. Banking System
Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches.
https://www.mdpi.com/2227-9091/14/4/73
Github repository:
https://github.com/moralesmendozar/financialFlowNetworks
Job Market paper
The power of good neighbors
An analysis of intergenerational mobility
This paper provides a theoretical and quantitative analysis of the role of good neighbors (i.e., neighbors with better information signals about the current state of the economy) for saving and human capital formation outcomes. It proposes a framework that links the spillover effects of optimal neighborhood selection with inequality and network analysis and estimation. This research contributes to the growing literature on intergenerational mobility. Theoretically, it introduces a novel network-based model of information cluster formation, incorporating endogenous neighborhood selection. Living in neighborhoods with high connectivity produces information spillovers, so that better financial choices result from those connections and are ultimately reflected in savings decisions and wealth accumulation. Similarly, the spillover affects the choice of parents on their children’s education, further enhancing wealth accumulation over time. A network is more homogeneous (heterogeneous) when the agents are more (less) connected at the same information level. The study characterizes the unobservable parameter values, returns of investment and precision signal of the agents, for which there is a stable homogeneous outcome that does not maximize the sum of expected returns of all the agents. The unobserved parameters are estimated with data from the Opportunity Atlas. Quantitatively, the compensating value in welfare for a low-information household (an agent with a less precise signal) of living in a neighborhood with 25% of high-information neighbors, instead of none, is estimated to be roughly 16% of the median saving. Furthermore, the simulation based on the estimated parameters in the network structure predicts a ten-percent decrease in Economic Connectedness – the fraction of high-informed friends among low-informed individuals – for the next generation, compared to the benchmark of a random network.
Research Assistant work
