Economics (E) Session 2
Time and Date: 14:15  15:45 on 19th Sep 2016
Room: B  Berlage zaal
Chair: Roland Kupers
14  Vulnerability of Banking Networks Against Financial Contagion: Measures, Evaluations and Implications
[abstract]
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 socalled 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 overthecounter 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 FINSTAB for computing the stability is available from the website www2.cs.uic.edu/~dasgupta/financialsimulatorfiles.
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Piotr Berman, Bhaskar Dasgupta, Lakshmi Kaligounder and Marek Karpinski 
437  Financial complexity: network reconstruction, systemic risk, and earlywarning signals
[abstract]
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 higherorder
properties are correctly predicted? In this talk, I will present a
general maximumentropy 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 counterexample, 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 earlywarning signals of the
upcoming crisis. By definition, these early warnings are undetectable
if the network is reconstructed from partial bankspecific information.
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Diego Garlaschelli 
581  Dynamics in two networks based on stocks of the US stock market
[abstract]
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 nonsymmetric 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.
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Leonidas Sandoval 
186  Entangling credit and funding shocks in interbank markets
[abstract]
Abstract: Credit and liquidity risks represent main channels of financial contagion for interbank lending markets. On one hand, banks face potential losses whenever their counterparties 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 salesthat 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 DebtSolvency 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 stresstest 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.
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Giulio Cimini and Matteo Serri 
423  EXPLORING THE COUNTERPARTYLIQUIDITY RISK NEXUS USING A MULTIAGENT NETWORK MODEL OF THE INTERBANK MARKET
[abstract]
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 powerlaw distribution. Citing the literature finding that interbank network typically exhibit a coreperiphery architecture, we apply a fitnessbased 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 visavis their securities portfolios. This results in a bipartite
graph representation.
The AgentBased 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.
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Nicolas K. Scholtes 