2015/12/31 by Marco Bardoscia, Fabio Caccioli, Juan I. Perotti +3 · 96 citations
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Banking stability, regulation, efficiency #Biology #Complex Systems and Time Series Analysis #Computer science #Credit Risk and Financial Regulations #Creditor #Distress #Econometrics #Economics #Financial contagion #Financial crisis #Financial distress #Financial system #Machine learning #Mathematics #Physics #Set (abstract data type) #Simple (philosophy) #Stability (learning theory) #Topology (electrical circuits) #physics.soc-ph #q-fin.RM
paper · pdf · doi:10.1371/journal.pone.0163825
published in PLoS ONE 11(10), e0163825 (Public Library of Science) · 9 pages, 5 figures
arxiv created 2016/09/27 · openalex publication_date 2016/10/04 · arxiv updated 2016/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.