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

Abstract

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.

Market procyclicality and systemic risk

“…Hence, the failure of one bank i in the core is likely to coincide with the failure of all of the other banks. This is also consistent with recent empirical studies (see e.g., Battiston et al, 2012c). We define systemic default as an event in which, at any time t ≥ 0, the leverage φ is equal to or greater than one.…”

“…Indeed, network density has been found to be a major driver of systemic risk (see e.g., Battiston et al, 2012a,b;Tasca and Battiston, 2011). There is also a body of empirical evidence that suggests that financial networks typically display a core-periphery structure with a dense core and a sparsely connected periphery (see e.g., Battiston et al, 2012c;Cont et al, 2011;Iori et al, 2006;Vitali et al, 2011). In the following, our aim is to describe the dynamics of the banks in the core of a core-periphery structure.…”

The Price of Complexity in Financial Networks

“…The resulting complexity of the financial system is a potential source of information asymmetries, collective moral hazard, and increased systemic risk (16)(17)(18)(19)(20) and, hence, requires deeper understanding. In particular, the determination of the probability of systemic events has remained an open problem so far (21)(22)(23)(24)(25). Here, we show that, in a network of financial contracts, the probability of systemic default can be very sensitive to errors on information about contracts as well as on information about the complexity of the network structure.…”

Towards a Complexity-Friendly Policy: Breaking the Vicious Circle of Equilibrium Thinking in Economics and Public Policy

“…By considering the role of financial networks in endogenously generating system risk, recent studies on financial networks have shown that this policy is not sufficient or even counterproductive [51]. For example, using an empirical dataset on the USD 1.2 trillion FED emergency loans programme during 2008-2010, Battiston et al [6] built a DeptRank indicator that mapped the links between financial institutions in terms of financial dependence (e.g. dept distribution and equity investment relations) and, similar to PageRank and other feedback centrality indicators, measured the impact of a distressed node (e.g.…”