Sharp Transitions and Systemic Risk in Sparse Financial Networks
Riley James Bendel
TL;DR
The paper investigates contagion and systemic risk in sparse directed financial networks by modeling balance-sheet interactions on a directed Erdős–Rényi graph and introducing a sender-truncated, single-hit contagion mechanism via $G_{sh}$. It develops a two-regime analysis: in the subcritical case $\rho_{out}<1$, forward reachability is polylogarithmic and shocks trigger cascades of size $O((\log n)^2)$ with high probability, with random shocks having negligible multi-hit defaults due to a deferred-decisions argument. In the supercritical case $\rho_{out}>1$, $G_{sh}$ is identified as an i.i.d.-outdegree digraph, enabling a Bow-tie/strong-giant framework and yielding a sharp random-shock threshold; however, monotone sharp-threshold methods do not apply because edge additions can dilate per-edge exposure, motivating the focus on the monotone substructure. Overall, the work clarifies how sparsity, exposure allocation, and a single-hit contagion mechanism jointly determine when and how systemic events emerge, linking branching-process techniques with random-graph structure results to derive precise probabilistic statements about cascade size and thresholds.
Abstract
We study contagion and systemic risk in sparse financial networks with balance-sheet interactions on a directed random graph. Each institution has homogeneous liabilities and equity, and exposures along outgoing edges are split equally across counterparties. A linear fraction of institutions have zero out-degree in sparse digraphs; we adopt an external-liability convention that makes the exposure mapping well-defined without altering propagation. We isolate a single-hit transmission mechanism and encode it by a sender-truncated subgraph G_sh. We define adversarial and random systemic events with shock size k_n = c log n and systemic fraction epsilon n. In the subcritical regime rho_out < 1, we prove that maximal forward reachability in G_sh is O(log n) with high probability, yielding O((log n)^2) cascades from shocks of size k_n. For random shocks, we give an explicit fan-in accumulation bound, showing that multi-hit defaults are negligible with high probability when the explored default set is polylogarithmic. In the supercritical regime, we give an exact distributional representation of G_sh as an i.i.d.-outdegree random digraph with uniform destinations, placing it within the scope of the strong-giant/bow-tie theorem of Penrose (2014). We derive the resulting implication for random-shock systemic events. Finally, we explain why sharp-threshold machinery does not directly apply: systemic-event properties need not be monotone in the edge set because adding outgoing edges reduces per-edge exposure.