
TL;DR
This paper introduces a statistical model for weighted reciprocity in interbank networks, revealing how reciprocity patterns change over time and impact network structure analysis.
Contribution
It develops an exponential random graph model accounting for reciprocal links at both topological and weighted levels, providing exact solutions and applying it to real interbank data.
Findings
Higher-than-expected reciprocity before the financial crisis
Reciprocity diminishes during the crisis, especially among smaller banks
Neglecting reciprocity can lead to incorrect inferences about network triads
Abstract
Weighted reciprocity between two agents can be defined as the minimum of sending and receiving value in their bilateral relationship. In financial networks, such reciprocity characterizes the importance of individual banks as both liquidity absorber and provider, a feature typically attributed to large, intermediating dealer banks. In this paper we develop an exponential random graph model that can account for reciprocal links of each node simultaneously on the topological as well as on the weighted level. We provide an exact expression for the normalizing constant and thus a closed-form solution for the graph probability distribution. Applying this statistical null model to Italian interbank data, we find that before the great financial crisis (i) banks displayed significantly more weighted reciprocity compared to what the lower-order network features (size and volume distributions)…
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Taxonomy
MethodsSparse Evolutionary Training
