Contagious Synchronization and Endogenous Network Formation in Financial Networks

When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observing a larger group of peers conveys more information and thus leads to a stronger social belief. Extending the standard model of Bayesian updating in social networks, we show that the probability that banks synchronize their investment strategy on a state non-matching action critically depends on the weighting between private and social belief. This effect is alleviated when banks choose their peers endogenously in a network formation process, internalizing the externalities arising from social learning.Comment: 41 pages, 10 figures, Journal of Banking & Finance 201

Systemic risk in a network model of interbank markets with central bank activity

The breakdown of the interbank money markets in the face of the recent financial crisis has forced central banks and governments to take extraordinary measures to sustain financial stability. In this paper we investigate which influence central bank activity has on interbank markets. In our model, banks optimize a portfolio of risky investments and riskless excess reserves according to their risk and liquidity preferences. They are linked via interbank loans and face a stochastic supply of household deposits. We then introduce a central bank into the model and show that central bank activity enhances financial stability. We model the default of a large bank and analyse the resulting contagion effects. This is compared to a common shock that hits banks who have invested in similiar assets. Our results indicate that common shocks are not subordinate to contagion effects, but are instead the greater threat to systemic stability.systemic risk, interbank markets, monetary policy, contagion, common shocks

The effect of the interbank network structure on contagion and common shocks

This paper proposes a dynamic multi-agent model of a banking system with central bank. Banks optimize a portfolio of risky investments and riskless excess reserves according to their risk, return, and liquidity preferences. They are linked via interbank loans and face stochastic deposit supply. Evidence is provided that the central bank stabilizes interbank markets in the short-run only. Comparing different interbank network structures, it is shown that money-center networks are more stable than random networks. Systemic risk via contagion is compared to common shocks and it is shown that both forms of systemic risk require different optimal policy responses. --systemic risk,contagion,common shocks,multi-agent simulations

Revealing patterns of local species richness along environmental gradients with a novel network tool

How species richness relates to environmental gradients at large extents is commonly investigated aggregating local site data to coarser grains. However, such relationships often change with the grain of analysis, potentially hiding the local signal. Here we show that a novel network technique, the “method of reflections”, could unveil the relationships between species richness and climate without such drawbacks. We introduced a new index related to potential species richness, which revealed large scale patterns by including at the local community level information about species distribution throughout the dataset (i.e., the network). The method effectively removed noise, identifying how far site richness was from potential. When applying it to study woody species richness patterns in Spain, we observed that annual precipitation and mean annual temperature explained large parts of the variance of the newly defined species richness, highlighting that, at the local scale, communities in drier and warmer areas were potentially the species richest. Our method went far beyond what geographical upscaling of the data could unfold, and the insights obtained strongly suggested that it is a powerful instrument to detect key factors underlying species richness patterns, and that it could have numerous applications in ecology and other fields