Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

We analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas

The determinants of interbank contagion: do patterns matter?

The recent financial crisis highlighted that interconnectedness between banks has a crucial role, and can push the effects of bank defaults to extreme levels. Interconnectedness in banking systems can be modelled trough the interbank market structure. As only data on interbank credits and debts aggregated at bank level are publicly available, one common hypothesis is to assume that banks maximise the dispersion of their interbank credits and debts, so that the interbank matrix is approximated by its maximum entropy realisation. The aim of this paper is to test the influence of this approximation on simulations, and verifying if variations in the structure of the interbank matrix systematically change the magnitude of contagion. Numerical experiments on samples of banks from four European countries, showed that different interbank matrices produce small changes in the point estimation. Nevertheless, they significantly affect variability and confidence interval for the estimates, in particular in banking systems when contagion effects are more intense

Methods for Reconstructing Interbank Networks from Limited Information: A Comparison

In this chapter, we review and compare some methods for the reconstruction of an interbank network from limited information. By exploiting the theory of complex networks and some ideas from statistical physics, we mainly focus on three different methods based on the maximum entropy principle, the relative entropy minimization, and the fitness model. We apply our analysis to the credit network of electronic Market for Interbank Deposit (e-MID) in 2011. In comparing the goodness of fit of the proposed methods, we look at the topological network properties and how reliably each method reproduces the real-world network

Systemic risk analytics: A data-driven multi-agent financial network (MAFN) approach

Systemic risk from financial intermediaries (FIs) refers to a negative externality problem, which is rife with fallacy of composition-type errors. To 'see' why seemingly rational behaviour at the level of an individual FI contributes to system-wide instability is a non-trivial exercise, which requires holistic visualization and modelling techniques. Paradox of volatility inherent to market price-based measures of systemic risk has made bilateral balance sheet and off balance data between FIs and network analysis essential for systemic risk management. There is both a data and a skills gap in implementing large-scale data-driven multi-agent financial network models that can operationalize macro-prudential policy. Different designs for a Pigou-type systemic risk surcharge are discussed with special reference to the Markose eigen-pair method, which simultaneously determines the degree of instability of the network of financial flows of obligors and also the rank order in the centrality of FIs contributing to it. © 2013 Macmillan Publishers Ltd