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The Cross-Section of Interbank Rates: A Nonparametric Empirical Investigation

This paper analyzes the distribution of lending and borrowing credit spreads in the European interbank market conditional on main features of banks such as their size, operating currency and nationality. This is done by means of nonparametric kernel estimation methods for the cross-sectional density of interbank funding rates over a large sample of European banks trading in the e-MID market. The analysis is repeated over consecutive non-overlapping periods in order to assess and compare the effect of the factors during crisis and non-crisis periods.

DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk

Systemic 4 risk, here meant as the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. However, there is no widely accepted methodology to determine the systemically important nodes in a network. To fill this gap, we introduce, DebtRank, a novel measure of systemic impact inspired by feedback-centrality. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008–2010.

Robustness and assortativity for Diffusion-like Processes in Scale-Free Networks

By analysing the diffusive dynamics of epidemics and of distress in complex networks, we study the effect of the assortativity on the robustness of the networks. We first determine by spectral analysis the thresholds above which epidemics/failures can spread; we then calculate the slowest diffusional times. Our results shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize, while in assortative networks there is a longer time for intervention before epidemic/failure spreads.

Networks with arbitrary edges multiplicities

One of the main characteristics of real-world networks is their large clustering. Clustering is one aspect of a more general but much less studied structural organization of networks, i.e. edge multiplicity, defined as the number of triangles in which edges, rather than vertices, participate. Here we show that the multiplicity distribution of real networks is in many cases scale free, and in general very broad.

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