Emergence of frustration signals systemic risk

TL;DR

This paper demonstrates that systemic risk in complex systems, such as financial markets, can be detected through the analysis of evolving interaction networks, revealing frustration, delocalization, and core-periphery structures during crises.

Contribution

It introduces a network-based approach to identify systemic risk by analyzing fluctuation correlations and structural balance in financial interaction networks over time.

Findings

Crisis periods show loss of structural balance and increased network coherence.

Emergence of core-periphery organization correlates with systemic crises.

Eigenmodes become delocalized during crises, indicating collective behavior.

Abstract

We show that the emergence of systemic risk in complex systems can be understood from the evolution of functional networks representing interactions inferred from fluctuation correlations between macroscopic observables. Specifically, we analyze the long-term collective dynamics of the New York Stock Exchange between 1926-2016, showing that periods marked by systemic crisis, viz., around the Great Depression of 1929-33 and the Great Recession of 2007-09, are associated with emergence of frustration indicated by the loss of structural balance in the interaction networks. During these periods the dominant eigenmodes characterizing the collective behavior exhibit delocalization leading to increased coherence in the dynamics. The topological structure of the networks exhibits a slowly evolving trend marked by the emergence of a prominent core-periphery organization around both of the crisis periods.