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Centrality Measurement of the Mexican Large Value Payments System from the Perspective of Multiplex Networks

Author

Listed:
  • Bernardo Bravo-Benitez
  • Biliana Alexandrova-Kabadjova
  • Serafin Martinez-Jaramillo

Abstract

With the purpose of going further in the understanding of the payment flows among the participants in the large value payment system in Mexico, SPEI, we elaborate payment networks using historical data for a period of seven years. We conceptualize the SPEI large value payment system as a multiplex network and we study it accordingly. Based on transactions performed on a daily basis, we present three layers built on the following types of payments, i.e. transactions sent from participant to participant, from participant to third party and from third party to third party. We observe that those layers exhibit dissimilar topology: the participant to participant layer reveals the behaviour of banks settling their own obligations, which proved to be sensitive to the failure of Lehmann Brothers; the participant to third party payments layer presented stable properties; and the third party to third party layer resulted in an increasingly dense network since the system has been adopted for the settlement of low-value obligations between accountholders. In order to identify relevant players in those layers, we compare some well-known centrality measures and also a novel centrality measure specifically designed for payment systems, SinkRank. The rankings assigned by SinkRank show a low degree of coincidence across layers. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Bernardo Bravo-Benitez & Biliana Alexandrova-Kabadjova & Serafin Martinez-Jaramillo, 2016. "Centrality Measurement of the Mexican Large Value Payments System from the Perspective of Multiplex Networks," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 19-47, January.
  • Handle: RePEc:kap:compec:v:47:y:2016:i:1:p:19-47
    DOI: 10.1007/s10614-014-9477-0
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    Cited by:

    1. Seabrook, Isobel & Caccioli, Fabio & Aste, Tomaso, 2022. "Quantifying impact and response in markets using information filtering networks," LSE Research Online Documents on Economics 115308, London School of Economics and Political Science, LSE Library.
    2. Isobel Seabrook & Fabio Caccioli & Tomaso Aste, 2021. "An Information Filtering approach to stress testing: an application to FTSE markets," Papers 2106.08778, arXiv.org.
    3. Yanquen, Eduardo & Livan, Giacomo & Montañez-Enriquez, Ricardo & Martinez-Jaramillo, Serafin, 2022. "Measuring systemic risk for bank credit networks: A multilayer approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    4. Caceres-Santos, Jonnathan & Rodriguez-Martinez, Anahi & Caccioli, Fabio & Martinez-Jaramillo, Serafin, 2020. "Systemic risk and other interdependencies among banks in Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    5. George Halkos & Shunsuke Managi & Kyriaki Tsilika, 2021. "Ranking Countries and Geographical Regions in the International Green Bond Transfer Network: A Computational Weighted Network Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1301-1346, December.

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    More about this item

    Keywords

    Payment systems; Financial stability; Network modeling; G21; G28; C6; C8;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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