Abstract: Instabilities of major financial institutions during the recent financial crisis of 2007 and later have generated renewed interests in evaluating the stabilities (or, lack thereof) of banking networks among economists, regulatory authorities and other relevant segments of the population. In particular, one reason of such type of vulnerabilities to the so-called financial contagion process in which failures of few individual banks propagate through the "web of banking dependencies" to affect a significant part of the entire global banking system. Motivated by such observations, we consider the problem of defining and evaluating stabilities of both homogeneous and heterogeneous banking networks against propagation of synchronous idiosyncratic shocks given to a subset of banks. We formalize an extension of a financial network model originally proposed by Nier et al. for scenarios such as the over-the-counter derivatives market and its corresponding heterogeneous version, formalize the synchronous shock propagation procedures, define two appropriate stability measures and investigate the computational complexities of evaluating these measures for various network topologies and parameters of interest. We next perform a comprehensive evaluation of these stability measures over more than 700,000 combinations of networks types and parameter combinations. Based on our evaluations, we discover many interesting implications of our evaluations of the stability measures, and derive topological properties and parameter combinations that may be used to flag the network as a possible fragile network. An interactive software FIN-STAB for computing the stability is available from the website www2.cs.uic.edu/~dasgupta/financial-simulator-files.

Abstract: The global financial crisis shifted the interest from traditional measures of “risk” of individual banks to new measures of “systemic risk”, defined as the risk of collapse of an entire interbank system. In principle, estimating systemic risk requires the knowledge of the whole network of exposures among banks. However, due to confidentiality issues, banks only disclose their total exposure towards the aggregate of all other banks, rather than their individual exposures towards each bank. Is it possible to statistically reconstruct the hidden structure of a network in such a way that privacy is protected, but at the same time higher-order properties are correctly predicted? In this talk, I will present a general maximum-entropy approach to the problem of network reconstruction and systemic risk estimation. I will illustrate the power of the method when applied to various economic, social, and biological systems. Then, as a counter-example, I will show how the Dutch interbank network started to depart from its reconstructed counterpart in the three years preceding the 2008 crisis. Over this period, many topological properties of the network showed a gradual transition to the crisis, suggesting their usefulness as early-warning signals of the upcoming crisis. By definition, these early warnings are undetectable if the network is reconstructed from partial bank-specific information.

Abstract: We follow the main stocks belonging to the New York Stock Exchange and to Nasdaq from 2003 to 2012, through years of normality and of crisis, and study the dynamics of networks built on two measures expressing relations between those stocks: correlation, which is symmetric and measures how similar two stocks behave, and Transfer Entropy, which is non-symmetric and measures the influence of the time series of one stock onto another in terms of the information that the time series of one stock transmits to the time series of another stock. The two measures are used in the creation of two networks that evolve in time, revealing how the relations between stocks and between industrial sectors changed in times of crisis. The two networks are also used in conjunction with a dynamic model of the spreading of volatility in order to detect which are the stocks that are most likely to spread crises, according to the model. This information may be used in the building of policies aiming to reduce the effects of financial crises.

Abstract: Credit and liquidity risks represent main channels of financial contagion for interbank lending markets. On one hand, banks face potential losses whenever their counter-parties are under distress and thus unable to fulfill their obligations. On the other hand, solvency constraints may force banks to recover lost fundings by selling their illiquid assets, resulting in effective losses in the presence of fire sales---that is, when funding shortcomings are widespread over the market. Because of the complex structure of the network of interbank exposures, these losses reverberate among banks and eventually get amplified, with potentially catastrophic consequences for the whole financial system. Building on Debt Rank [Battiston et al., 2012], in this work we define a systemic risk metric that estimates the potential amplification of losses in interbank markets accounting for both credit and liquidity contagion channels: the Debt-Solvency Rank. We implement this framework on a dataset of 183 European banks that were publicly traded between 2004 and 2013, showing indeed that liquidity spillovers substantially increase systemic risk, and thus cannot be neglected in stress-test scenarios. We also provide additional evidence that the interbank market was extremely fragile up to the 2008 financial crisis, becoming slightly more robust only afterwards.

Abstract: We simulate a bilayer network comprising the two channels mentioned above. To generate the direct exposure network, we draw sample of bank sizes (refecting the size of their balance sheets) from a truncated power-law distribution. Citing the literature finding that interbank network typically exhibit a core-periphery architecture, we apply a fitness-based model that connects the nodes/banks according to their sizes. Using this algorithm, we obtain an undirected, unweighted network with a densely connected core of large banks and a sparsely connected periphery who's connections are mainly to the core. The network of overlapping port- folios is simulated using a simple random graph generation algorithm based on calibrations for the average diversification of banks vis-a-vis their securities portfolios. This results in a bipartite graph representation. The Agent-Based Model (ABM) begins with an idiosyncratic deposit shock whose sign determines banks' role as borrowers or lenders on the interbank market. Following this, borrowers distribute their aggregate liquidity requirement across their local network of counterparties. After borrower liquidity requests have been transmitted to lenders, the next step is to determine final loan volumes as well as interbank interest rates. In the next step, borrowers repay their loans following an asset price shock. In order to meet their contractual obligations and comply with policy constraints (capital ratio and a minimum reserve requirement), banks sell of a fraction of their assets. The total volume of firesalesthen puts further downward pressure on asset prices and thus impacts all banks holding the distressed assets in their portfolios.