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Finding Communities in Credit Networks

In this paper the authors focus on credit connections as a potential source of systemic risk. In particular, they seek to answer the following question: how do we find densely connected subsets of nodes within a credit network? The question is relevant for policy, since these subsets are likely to channel any shock affecting the network. As it turns out, a reliable answer can be obtained with the aid of complex network theory. In particular, the authors show how it is possible to take advantage of the ‘community detection’ network literature.

Statistical ensembles for money and debt

We all build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann–Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation.

Leveraged Network-Based Financial Accelerator

In this paper we build on the network-based financial accelerator model of Delli Gatti et al. (2010), modelling the firms’ financial structure following the “dynamic trade-off theory”, instead of the “pecking order theory”. Moreover, we allow for multiperiodal debt structure and consider multiple bank-firm links based on a myopic preferred-partner choice. In case of default, we also consider the loss given default rate (LGDR).

Liaisons Dangereuses: Increasing Connectivity, Risk Sharing, and Systemic Risk

The recent financial crisis poses the challenge to understand how systemic risk arises endogenously and what architecture can make the financial system more resilient to global crises. This paper shows that a financial network can be most resilient for intermediate levels of risk diversification, and not when this is maximal, as generally thought so far. This finding holds in the presence of the financial accelerator, i.e.

Default Cascades: When Does Risk Diversification Increase Stability?

We explore the dynamics of default cascades in a network of credit interlink-ages in which each agent is at the same time a borrower and a lender. When some counterparties of an agent default, the loss she experiences amounts to her total exposure to those counterparties. A possible conjecture in this context is that individual risk diversification across more numerous counterparties should make also systemic defaults less likely. We show that this view is not always true.