Filling in the blanks: network structure and interbank contagion
By Kartik Anand, Ben R. Craig, Goetz von Peter. Social Science Research Network, 2014
236 citations recorded by the source corpus.
The network pattern of financial linkages is important in many areas of banking and finance. Yet, bilateral linkages are often unobserved, and maximum entropy serves as the leading method for estimating counterparty exposures. This paper proposes an efficient alternative that combines information-theoretic arguments with economic incentives to produce more realistic interbank networks that preserve important characteristics of the original interbank market. The method loads the most probable links with the largest exposures consistent with the total lending and borrowing of each bank, yielding networks with minimum density. When used in a stress-testing context, the minimum-density solution overestimates contagion, whereas maximum entropy underestimates it. Using the two benchmarks side-by-side defines a useful range that bounds the cost of contagion in the true interbank network when counterparty exposures are unknown.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/0bb806ff4335eaab425087a9d4129c558fdce049
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Citations in the corpus
- Transmission channels of systemic risk and contagion in the European financial network (2015, methodology): “A network describes a collection of nodes and the links between them, and thus, by using a network representation the structure of linkages among financial institutions (i.e. nodes) can be modelled and measured.”
- Too Interconnected to Fail: A Survey of the Interbank Networks Literature (2015, methodology): “Alternative estimation methods, such as minimum density (Anand et al., 2014b), are currently being developed.”
- Bank Networks: Contagion, Systemic Risk and Prudential Policy (2017, unknown): “As noted by Bargigli et al. (2015), interbank networks tend to be dis-assortative, implying that high-degree nodes tend to connect to other high-degree nodes less frequently than would be expected under the assumption of a random rewiring of the network that preserves the nodes’ degrees.”