[Paper Review] Network Sensitivity of Systemic Risk

Amanah Ramadiah, Domenico Di Gangi+7 more|SSRN Electronic Journal|May 11, 2018

Complex Systems and Time Series Analysis4 citations

TL;DR

This paper investigates how systemic risk in financial networks depends on topological features such as density, block structure (core-periphery and modular), and shock propagation dynamics. Using a generalized network reconstruction method based on real balance sheet data, it demonstrates that systemic risk is highly sensitive to network structure—especially core-periphery architectures and inter-block connectivity—highlighting that small structural changes can drastically alter systemic vulnerability under distress contagion.

ABSTRACT

A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put in modeling the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets - either using real network data, reconstruction techniques or simple toy networks. Here we address the more general problem of how shock propagation dynamics depends on the topological details of the underlying network. To this end we consider different realistic network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets.

Motivation & Objective

• To understand how topological features of financial networks influence systemic risk propagation under different shock dynamics.
• To generalize a financial network reconstruction method to include weighted, heterogeneous networks with core-periphery and modular block structures.
• To assess the sensitivity of systemic risk to network density, modularity, and assortativity in realistic financial network topologies.
• To evaluate the impact of different contagion mechanisms—default vs. distress contagion—on systemic outcomes across network structures.
• To provide actionable insights for regulators on designing more resilient financial network architectures.

Proposed method

• Generalized the Anand et al. (2018) network reconstruction method to generate weighted, heterogeneous financial networks with tunable core-periphery and modular block structures.
• Used a parameter φ to continuously interpolate between random networks (φ = 0) and core-periphery networks (φ = 1), enabling systematic study of structural transitions.
• Applied DebtRank centrality with a tunable confidence parameter α to model distress contagion, where α → ∞ corresponds to default contagion.
• Simulated shock propagation using uniform initial shocks across all institutions and analyzed systemic losses across varying network densities and topologies.
• Explored both assortative and disassortative modular structures to model cross-country or lender-borrower market dynamics.
• Quantified systemic risk via total equity losses across the network, comparing outcomes across different network types and shock mechanisms.

Experimental results

Research questions

• RQ1 How does network density affect systemic risk propagation in financial networks with realistic balance sheet data?
• RQ2 How do core-periphery and modular network structures influence the vulnerability of financial systems to systemic shocks?
• RQ3 What is the relative impact of default contagion versus distress contagion (via DebtRank) on systemic risk outcomes?
• RQ4 Does the presence of inter-block connections in modular networks lead to discontinuous jumps in systemic risk at certain density thresholds?
• RQ5 How do structural features like degree assortativity and block modularity affect the resilience of financial networks under uniform shocks?

Key findings

• Systemic risk is highly sensitive to network topology: core-periphery structures are significantly less resilient than random or modular networks under uniform shocks.
• In core-periphery networks, the core region is robust to shocks, but peripheral institutions are highly vulnerable, especially as network density increases.
• For modular networks, an assortative block structure leads to a sharp increase in systemic risk above a critical inter-block connection density, indicating a phase-like transition.
• In contrast, disassortative modular structures show a gradual decline in systemic risk as inter-block connectivity increases, with no abrupt transition.
• Distress contagion (via finite α in DebtRank) reveals systemic vulnerabilities before any default occurs, unlike default contagion which only triggers losses upon actual defaults.
• The 2008 financial crisis regime showed a bimodal response to confidence (α): low confidence led to widespread losses, while high confidence limited propagation, unlike in 2013 where low confidence did not trigger systemic collapse.