Systemic Losses Due to Counterparty Risk in a Stylized Banking System

Abstract

We report a study of a stylized banking cascade model investigating systemic risk caused by counterparty failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to distressed whenever its liabilities are larger than the banks' assets. The banks are connected through an interbank lending network and, whenever a bank is distressed, its creditor cannot expect the loan from the distressed bank to be repaid, potentially becoming distressed themselves. We solve the problem analytically for a homogeneous system and test the robustness and generality of the results with simulations of more complex systems. We investigate the parameter space and the corresponding distribution of operating banks mapping the conditions under which the whole system is stable or unstable. This allows us to determine how financial stability of a banking system is influenced by regulatory decisions, such as leverage; we discuss the effect of central bank actions, such as quantitative easing and we determine the cost of rescuing a distressed banking system using re-capitalisation. Finally, we estimate the stability of the UK and US banking systems comparing the years 2007 and 2012 by using real data.

Relation between regional uncertainty spillovers in the global banking system

“…The literature on spillover effects can be broadly classified into two categories. The first category comprises network models which aim to describe various causal mechanics of financial contagion, which can be calibrated with balance-sheet data (Furfine, 2003;Degryse and Nguyen, 2007;Upper and Worms, 2004;Müller, 2006;Cont et al, 2010;Upper, 2011;Birch and Aste, 2014). The second category comprises econometric models, which aim at identifying spillover effects exclusively from market data, without making assumptions about the dynamics of distress propagation between banks (Adrian and Brunnermeier, 2016;Brownlees and Engle, 2016).…”

Editors' foreword

“…The application to other areas of risk in financial institutions was limited because of the restrictions imposed by the regulators and the methods that were acceptable by the early Basel accords. After the credit crisis of 2008 and thanks to the flexibility of Basel III, many new methods combining ML and social network analysis have been explored to evaluate systemic risk (Angelini et al 2008, Koyuncugil and Ozgulbas 2012, Bao and Datta 2014, Birch and Aste 2014, O'Halloran et al 2015, Fraiberger 2016, Manela and Moreira 2017, Van Liebergen 2017, Hanley and Hoberg Forthcoming, Glasserman and Mamaysky 2019 as well as alternative calculations of Value-at-Risk and its components: credit, market and operational risk. In the last few years, mainstream econometricians and finance researchers have also incorporated the use of ML methods to solve problems of asset pricing and financial forecasting.…”

Can banks default overnight? Modelling endogenous contagion on the O/N interbank market

“…http://www.nature.com/nphys/journal/v9/n3/index.html of the various layers of interbank relations matters for contagion assessment. Such studies show that the network structure determines the stability of the interbank market (for a contrasting view see Birch and Aste [14], who argue that the network structure has less importance).…”