Change Detection in Core-Periphery Networks: A Case Study on Detecting Financial Crises in the Interbank Market

13 Pages Posted: 27 Jan 2021

See all articles by Desheng Ma

Desheng Ma

Cornell University - School of Applied and Engineering Physics

Shawn Mankad

Cornell University

Date Written: December 3, 2020

Abstract

We develop and present a new methodology to detect regime changes within a sequence of sparse networks that have overlapping and evolving community structure. The core of the methodology is a non-negative matrix factorization that maximizes a Poisson likelihood subject to a penalty that accounts for sparsity in the network. By fitting the factorization model over a rolling window with a fast numerical optimization algorithm, change detection is accomplished by statistical monitoring of the matrix factors' evolution. Using synthetic and real financial interbank lending networks, we demonstrate that the proposed methodology compares favorably with alternative techniques for on-the-go network change detection.

Keywords: financial networks, interbank market, change point detection, matrix factorization

JEL Classification: C00, C18, G01, C44

Suggested Citation

Ma, Desheng and Mankad, Shawn, Change Detection in Core-Periphery Networks: A Case Study on Detecting Financial Crises in the Interbank Market (December 3, 2020). Available at SSRN: https://ssrn.com/abstract=3742790 or http://dx.doi.org/10.2139/ssrn.3742790

Desheng Ma

Cornell University - School of Applied and Engineering Physics ( email )

247 Clark Hall
Ithaca, NY 14853
United States

Shawn Mankad (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States
6072559594 (Phone)

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