Systemic risk in dynamical networks with stochastic failure criterion (original) (raw)

Systemic risk propagation and distress in bank networks

2017

Stability of the banking system and macro-prudential regulation are essential for healthy economic growth. In this paper we study the European bank network and its vulnerability to stressing different bank assets. The importance of macro-prudential policy is emphasized by the inherent vulnerability of the financial system, high level of leverage, interconnectivity of system’s entities, similar risk exposure of financial institutions, and susceptibility for systemic crisis propagation through the system. Current stress tests conducted by the European Banking Authority do not take in consideration the connectivity of the banks and the potential of one bank vulnerability spilling over to the rest of the system. We create a bipartite network with bank nodes on one hand and asset nodes on the other with weighted links between the two layers based on the level of different countries’ sovereign debt holdings by each bank. We propose a model for systemic risk propagation based on common ban...

Systemic Risk in Financial Networks

SSRN Electronic Journal, 1999

We consider default by firms that are part of a single clearing mechanism. The obligations of all firms within the system are determined simultaneously in a fashion consistent with the priority of debt claims and the limited liability of equity. We first show, via a fixed-point argument, that there always exists a "clearing payment vector" that clears the obligations of the members of the clearing system; under mild regularity conditions, this clearing vector is unique. Next, we develop an algorithm that both clears the financial network in a computationally efficient fashion and provides information on the systemic risk faced by system firm. Finally, we produce qualitative comparative statics for financial networks. These comparative statics imply that, in contrast to single-firm results, unsystematic, nondissipative shocks to the system will lower the total value of the network and may lower the value of the equity of some of the individual network firms.

Network models and financial stability

Journal of Economic Dynamics and Control, 2007

Systemic risk is a key concern for central banks charged with safeguarding overall financial stability. In this paper we investigate how systemic risk is affected by the structure of the financial system. We construct banking systems that are composed of a number of banks that are connected by interbank linkages. We then vary the key parameters that define the structure of the financial system -including its level of capitalisation, the degree to which banks are connected, the size of interbank exposures and the degree of concentration of the system -and analyse the influence of these parameters on the likelihood of contagious (knock-on) defaults. First, we find that the better capitalised banks are, the more resilient is the banking system against contagious defaults and this effect is non-linear. Second, the effect of the degree of connectivity is non-monotonic, that is, initially a small increase in connectivity increases the contagion effect; but after a certain threshold value, connectivity improves the ability of a banking system to absorb shocks. Third, the size of interbank liabilities tends to increase the risk of knock-on default, even if banks hold capital against such exposures. Fourth, more concentrated banking systems are shown to be prone to larger systemic risk, all else equal. In an extension to the main analysis we study how liquidity effects interact with banking structure to produce a greater chance of systemic breakdown. We finally consider how the risk of contagion might depend on the degree of asymmetry (tiering) inherent in the structure of the banking system. A number of our results have important implications for public policy, which this paper also draws out.

Propagation of Systemic Risk in Interbank Networks

2014

This work explores the characteristics of financial contagion in networks whose links distributions approaches a power law, using a model that defines banks balance sheets from information of network connectivity. By varying the parameters for the creation of the network, several interbank networks are built, in which the concentrations of debts and credits are obtained from links distributions during the creation networks process. Three main types of interbank network are analyzed for their resilience to contagion: i) concentration of debts is greater than concentration of credits, ii) concentration of credits is greater than concentration of debts and iii) concentrations of debts and credits are similar. We also tested the effect of a variation in connectivity in conjunction with variation in concentration of links. The results suggest that more connected networks with high concentration of credits (featuring nodes that are large creditors of the system) present greater resilience to contagion when compared with the others networks analyzed. Evaluating some topological indices of systemic risk suggested by the literature we have verified the ability of these indices to explain the impact on the system caused by the failure of a node. There is a clear positive correlation between the topological indices and the magnitude of losses in the case of networks with high concentration of debts. This correlation is smaller for more resilient networks.

A Systemic Stress Test Model in Bank-Asset Networks

Financial networks are dynamic and to assess systemic importance and avert losses we needs models which take the time variations of the links and nodes into account. We develop a model that can predict the response of the financial network to a shock and propose a measure for the systemic importance of the banks, which we call BankRank. Using the European Bank Authority 2011 stress test exposure data, we apply our model to the bipartite network of the largest institutional holders of troubled European countries (Greece, Italy, Portugal, Spain, and Ireland). Simulation of the states in our model reveal that it has "calm" state, where shocks do not cause very major losses, and "panicked" states, in which devastating damages occur. Fitting the parameters to Eurocrisis data shows that, before the crisis, the system was mostly in the "calm" regime while during the Eurocrisis it went into the "panicked" regime. The numerical solutions of the our mod...

Dynamic Models of Systemic Risk and Contagion

Informatics and Applications, 2017

Modern financial systems are complicated networks of interconnected financial institutions and default of one of them may have serious consequences for others. The recent crises have shown that the complexity and interconnectedness are major factors of systemic risk which became a subject of intensive studies usually concentrated on static models. In this paper we develop a dynamic model based on the so-called structural approach where defaults are triggered by the exit of some stochastic process from a domain. In our case, this is a process defined by the evolution of bank's portfolios values. At the exit time a bank defaults and a cascade of defaults starts. We believe that the distribution of the exit time and the subsequent losses may serve as indicators allowing regulators to monitor the state of the system to take corrective actions to avoid the contagion in the financial system. We model the development of financial system as a random graph using the preferable attachment algorithm and provide results of numerical experiments on simulated data.

Systemic risk in financial networks: a graph theoretic approach

2004

This paper puts forward a novel approach, based on the theory of network flows, for the analysis of default contagion in financial systems. We use a graph$theoretic representation of a financial network and of the flows of losses that can propagate across such a ...

Credit Risk Contagion and Systemic Risk on Networks

Mathematics, 2019

This paper proposes a model of the dynamics of credit contagion through non-performing loans on financial networks. Credit risk contagion is modeled in the context of the classical SIS (Susceptibles-Infected-Susceptibles) epidemic processes on networks but with a fundamental novelty. In fact, we assume the presence of two different classes of infected agents, and then we differentiate the dynamics of assets subject to idiosyncratic risk from those affected by systemic risk by adopting a SIIS (Susceptible-Infected1-Infected2-Susceptible) model. In the recent literature in this field, the effect of systemic credit risk on the performance of the financial network is a hot topic. We perform numerical simulations intended to explore the roles played by two different network structures on the long-term behavior of assets affected by systemic risk in order to analyze the effect of the topology of the underlying network structure on the spreading of systemic risk on the structure. Random gr...

Network Sensitivity of Systemic Risk

Journal of Network Theory in Finance, 2018

The recent stream of literature of systemic risk in financial markets emphasized the key importance of considering the complex interconnections among financial institutions. Much efforts has been put to model the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets---either using real data, reconstruction techniques or simple toy networks. Here we address the more general problem of how the shock propagation dynamics depends on the topological details of the underlying network. To this end, we consider different network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks with varying density and mesoscale structures, and vary as well the details of the shock propagation dynamics. We show that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can thus aid in the design of regulatory p...

Systemic risk, financial contagion and financial fragility

Journal of Economic Dynamics and Control, 2010

Although it is hard to arrive at a widely accepted definition for Systemic Risk; it is generally acknowledged that it is the risk of the occurrence of an event that threatens the well functioning of the system of interest (financial, payments, banking, etc.) sometimes to the point of making its operation impossible. We model systemic risk with two main components: a random shock that weakens one or more financial institutions and a transmission mechanism which transmits and possibly exacerbates such negative effects to the rest of the system. Our model could be conceptually represented by a network already described in previous works. In this work we show how is possible to estimate the distribution of losses for the banking system with our model. Additionally, we show how it is possible to separate the distribution of losses into two components: the losses incurred by the initial shock and the losses resulting from the contagion process. Finally, once the distribution is estimated, we can derive standard risk measures for the system as a whole. Another important contribution of this work is that we can follow the evolution of certain risk measures like the expected loss or the CVaR in order to evaluate if the system is becoming more or less risky, in fact, more or less fragile. Additionally, we can decompose the distribution of losses of the whole banking system into the systemic and the contagion elements and we can determine if the system is more prone to experience contagious difficulties during a certain period of time.