DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk

By Stefano Battiston, Michelangelo Puliga, Rahul Kaushik, Paolo Tasca, Guido Caldarelli. Scientific Reports, 2012

892 citations recorded by the source corpus.

Systemic risk, here meant as the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. However, there is no widely accepted methodology to determine the systemically important nodes in a network. To fill this gap, we introduce, DebtRank, a novel measure of systemic impact inspired by feedback-centrality. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008–2010. We find that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis. Moreover, a systemic default could have been triggered even by small dispersed shocks. The results suggest that the debate on too-big-to-fail institutions should include the even more serious issue of too-central-to-fail.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3c3c361ef96e7b636d075b715a97ae4c7ab36975

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Citations in the corpus

Multiplex Interbank Networks and Systemic Importance: An Application to European Data (2016, positive): “An exception is the recent contribution by Poledna et al. (2015), which uses the algorithm developed in Battiston et al. (2012) to study the multiplex Mexican interbank system.”
Too Interconnected to Fail: A Survey of the Interbank Networks Literature (2015, background): “Similarly, Battiston et al. (2012b) argue that due to a positive feedback loop resulting from a financial acceleration mechanism, more connections lead to greater fragility in the financial system.”
Quantifying China's Regional Economic Complexity (2017, background): “ problems in economics and statistical ﬁnance [26]. Recently, econophysicists have proposed network measures to reveal the true risks associated with institutions to make ﬁnancial markets more stable [27] and studied the complex correlations and trend switchings in ﬁnancial time series [28]. Moreove”
Vital nodes identification in complex networks (2016, methodology): “[359] took eigenvector centrality as the benchmark metric.”
Multi-Layered Interbank Model For Assessing Systemic Risk (2016, methodology): “In Battiston et al (2012) a measure based on network feedback centrality is introduced, the so-called DebtRank; this measure is used to analyze a dataset concerning the FED emergency loans program to global financial institutions during the period 2008-2010.”
Quantification of systemic risk from overlapping portfolios in the financial system (2018, background): “…financial institutions (Upper and Worms, 2002; Boss et al., 2004, 2005; Soramäki et al., 2007; Iori et al., 2008, 2015; Cajueiro et al., 2009; Bech and Atalay, 2010; Fricke and Lux, 2014), but also on the network of derivative exposures (Markose et al., 2012; Markose, I l i PRESS Journal of…”
Climate risks and financial stability (2021, background): “In addition, the high degree of interconnectedness of financial actors can further amplify losses for individual financial actors and for the financial sector, as occurred in the last financial crisis (Haldane and May, 2011; Billio et al., 2012; Battiston et al., 2012; Battiston et al., 2016).”