Structural Correlations in the Italian Overnight Money Market: An Analysis Based on Network Configuration Models
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Department of Economics, University of Kiel, 24118 Kiel, Germany
2
Observatoire Français des Conjonctures Economiques (OFCE)—Sciences Po, 06902 Sophia-Antipolis Cedex, France
*
Author to whom correspondence should be addressed.
Academic Editors: Stelios Bekiros and Michael (Mike) Stutzer
Entropy 2017, 19(6), 259; https://doi.org/10.3390/e19060259
Received: 21 March 2017 / Revised: 27 May 2017 / Accepted: 29 May 2017 / Published: 6 June 2017
(This article belongs to the Special Issue Entropic Applications in Economics and Finance)
We study the structural correlations in the Italian overnight money market over the period 1999–2010. We show that the structural correlations vary across different versions of the network. Moreover, we employ different configuration models and examine whether higher-level characteristics of the observed network can be statistically reconstructed by maximizing the entropy of a randomized ensemble of networks restricted only by the lower-order features of the observed network. We find that often many of the high order correlations in the observed network can be considered emergent from the information embedded in the degree sequence in the binary version and in both the degree and strength sequences in the weighted version. However, this information is not enough to allow the models to account for all the patterns in the observed higher order structural correlations. In particular, one of the main features of the observed network that remains unexplained is the abnormally high level of weighted clustering in the years preceding the crisis, i.e., the huge increase in various indirect exposures generated via more intensive interbank credit links.
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Keywords:
interbank network; structural correlations; clustering coefficients; configuration models; network reconstruction
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MDPI and ACS Style
Luu, D.T.; Lux, T.; Yanovski, B. Structural Correlations in the Italian Overnight Money Market: An Analysis Based on Network Configuration Models. Entropy 2017, 19, 259.
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