Abstract
Since the 2007–2009 financial crisis, substantial academic effort has been dedicated to improving our understanding of interbank lending networks (ILNs). Because of data limitations or by choice, the literature largely lacks multiple loan maturities. We employ a complete interbank loan contract dataset to investigate whether maturity details are informative of the network structure. Applying the layered stochastic block model of Peixoto (2015) and other tools from network science on a time series of bilateral loans with multiple maturity layers in the Russian ILN, we find that collapsing all such layers consistently obscures mesoscale structure. The optimal maturity granularity lies between completely collapsing and completely separating the maturity layers and depends on the development phase of the interbank market, with a more developed market requiring more layers for optimal description. Closer inspection of the inferred maturity bins associated with the optimal maturity granularity reveals specific economic functions, from liquidity intermediation to financing. Collapsing a network with multiple underlying maturity layers or extracting one such layer, common in economic research, is therefore not only an incomplete representation of the ILN’s mesoscale structure, but also conceals existing economic functions. This holds important insights and opportunities for theoretical and empirical studies on interbank market functioning, contagion, stability, and on the desirable level of regulatory data disclosure.
Highlights
Introduction to thestochastic block models (SBMs), the layered SBM and the coarse-grained layered SBM
In order to characterize the effect of aggregating maturity layers on the interbank lending networks (ILNs) mesoscale, we explicitly model the monthly ILNs by layered SBMs
We investigate the importance of loan maturity information in interbank lending networks towards understanding its mesoscale structure, i.e. the higher-level organisation of the banks into groups
Summary
Introduction to theSBM, the layered SBM and the coarse-grained layered SBM. SBMs are canonical models to study clustering and perform community detection[41,42]. When formulated in a Bayesian setting, the basic goal of SBMs is to determine the posterior probability distribution of all possible group assignments {bi} (where B = maxibi) given the observed network G, a quantity written as p({bi}|G). Because this is intractable for networks with more than a few nodes and edges, one is typically content with the mthaexoibmsuermveadpnoesttweroirokriGp.rMobaaxbiimlitiysi(nMg tAhPe)peossttiemriaoter,pi(.e{.bai}r|gGm) ainx{sbie}apr(c{hbi}f|oGr)t,hteo which one refers to as “the fit” to MAP estimate equivalently minimises the information-theoretic description length (DL) of the data G, i.e. Σ = −log p(G, {bi}) = S + L with.
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