The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected "hub" institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.
In this paper we present a novel method to reconstruct global topological properties of a complex network starting from limited information. We assume to know for all the nodes a non-topological quantity that we interpret as fitness. In contrast, we assume to know the degree, i.e. the number of connections, only for a subset of the nodes in the network. We then use a fitness model, calibrated on the subset of nodes for which degrees are known, in order to generate ensembles of networks. Here, we focus on topological properties that are relevant for processes of contagion and distress propagation in networks, i.e. network density and k-core structure, and we study how well these properties can be estimated as a function of the size of the subset of nodes utilized for the calibration. Finally, we also study how well the resilience to distress propagation in the network can be estimated using our method. We perform a first test on ensembles of synthetic networks generated with the Exponential Random Graph model, which allows to apply common tools from statistical mechanics. We then perform a second test on empirical networks taken from economic and financial contexts. In both cases, we find that a subset as small as 10 % of nodes can be enough to estimate the properties of the network along with its resilience with an error of 5 %.
We analyse time series of CDS spreads for a set of major US and European institutions in a period overlapping the recent financial crisis. We extend the existing methodology of -drawdowns to the one of joint -drawups, in order to estimate the conditional probabilities of spike-like co-movements among pairs of spreads. After correcting for randomness and finite size effects, we find that, depending on the period of time, 50% of the pairs or more exhibit high probabilities of joint drawups and the majority of spread series are trend-reinforced, i.e. drawups tend to be followed by drawups in the same series. We then carry out a network analysis by taking the probability of joint drawups as a proxy of financial dependencies among institutions. We introduce two novel centrality-like measures that offer insights on how both the systemic impact of each node as well as its vulnerability to other nodes' shocks evolve in time.
The intrinsic complexity of the financial derivatives market has emerged as both an incentive to engage in it, and a key source of its inherent instability. Regulators now faced with the challenge of taming this beast may find inspiration in the budding science of complex systems.
Financial inter-linkages play an important role in the emergence of financial instabilities and the formulation of systemic risk can greatly benefit from a network approach. In this paper, we focus on the role of linkages along the two dimensions of contagion and liquidity, and we discuss some insights that have recently emerged from network models. With respect to the issue of the determination of the optimal architecture of the financial system, models suggest that regulators have to look at the interplay of network topology, capital requirements, and market liquidity. With respect to the issue of the determination of systemically important financial institutions the findings indicate that both from the point of view of contagion and from the point of view of liquidity provision, there is more to systemic importance than just size. In particular for contagion, the position of institutions in the network matters and their impact can be computed through stress tests even when there are no defaults in the system.topology, capital requirements, and market liquidity. With respect to the issue of the determination of systemically important financial institutions the findings indicate that both from the point of view of contagion and from the point of view of liquidity provision, there is more to systemic importance than just size. In particular for contagion, the position of institutions in the network matters and their impact can be computed through stress tests even when there are no defaults in the system.
The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.
We implement a novel method to detect systemically important financial institutions in a network. The method consists in a simple model of distress and losses redistribution derived from the interaction of banks' balance-sheets through bilateral exposures. The algorithm goes beyond the traditional default-cascade mechanism, according to which contagion propagates only through banks that actually default. We argue that even in the absence of other defaults, distressed-but-non-defaulting institutions transmit the contagion through channels other than solvency: weakness in their balance sheet reduces the value of their liabilities, thereby negatively affecting their interbank lenders even before a credit event occurs. In this paper, we apply the methodology to a unique dataset covering bilateral exposures among all Italian banks in the period 2008-2012. We find that the systemic impact of individual banks has decreased over time since 2008. The result can be traced back to decreasing volumes in the interbank market and to an intense recapitalization process. We show that the marginal effect of a bank's capital on its contribution to systemic risk in the network is considerably larger when interconnectedness is high (good times): this finding supports the regulatory work on counter-cyclical (macroprudential) capital buffers.
