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Estimating the Structure of the Payment Network in the LVTS: An Application of Estimating Communities in Network Data

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  • James Chapman
  • Yinan Zhang

Abstract

In the Canadian large value payment system an important goal is to understand how liquidity is transferred through the system and hence how efficient the system is in settling payments. Understanding the structure of the underlying network of relationships between participants in the payment system is a crucial step in achieving the goal. The set of nodes in any given network can be partitioned into a number of groups (or "communities"). Usually, the partition is not directly observable and must be inferred from the observed data of interaction flows between all nodes. In this paper we use the statistical model of Copic, Jackson, and Kirman (2007) to estimate the most likely partition in the network of business relationships in the LVTS. Specifically, we estimate from the LVTS transactions data different "communities" formed by the direct participants in the system. Using various measures of transaction intensity, we uncover communities of participants that are based on both transaction amount and their physical locations. More importantly these communities were not easily discernible in previous studies of LVTS data since previous studies did not take into account the network (or transitive) aspects of the data.

Suggested Citation

  • James Chapman & Yinan Zhang, 2010. "Estimating the Structure of the Payment Network in the LVTS: An Application of Estimating Communities in Network Data," Staff Working Papers 10-13, Bank of Canada.
    • Handle: RePEc:bca:bocawp:10-13
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    References listed on IDEAS

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    1. Sean O'Connor & James Chapman & Kirby Millar, 2008. "Liquidity Efficiency and Distribution in the LVTS: Non-Neutrality of System Changes under Network Asymmetry," Discussion Papers 08-11, Bank of Canada.
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    Cited by:

    1. Bech, Morten L. & Bergstrom, Carl T. & Rosvall, Martin & Garratt, Rodney J., 2015. "Mapping change in the overnight money market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 44-51.
    2. Morten L. Bech & Carl T. Bergstrom & Rod Garratt & Martin Rosvall, 2011. "Mapping change in the federal funds market," Staff Reports 507, Federal Reserve Bank of New York.
    3. He, Yi & Wu, Shan & Tong, Mu, 2019. "Systemic risk and liquidity rescue in complex financial networks: Pit hole and black hole of liquidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Bewaji, Oluwasegun, 2024. "A computational model of bilateral credit limits in payment systems and other financial market infrastructures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(1).
    5. Martinez-Jaramillo, Serafin & Alexandrova-Kabadjova, Biliana & Bravo-Benitez, Bernardo & Solórzano-Margain, Juan Pablo, 2014. "An empirical study of the Mexican banking system’s network and its implications for systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 242-265.
    6. Brassil, Anthony & Nodari, Gabriela, 2021. "A Density-Based estimator of core/periphery network structures," Journal of Banking & Finance, Elsevier, vol. 125(C).
    7. Anthony Brassil & Gabriela Nodari, 2018. "A Density-based Estimator of Core/Periphery Network Structures: Analysing the Australian Interbank Market," RBA Research Discussion Papers rdp2018-01, Reserve Bank of Australia.

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      More about this item

      Keywords

      Payment clearing and settlement systems; Financial stability;

      JEL classification:

      • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
      • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
      • G20 - Financial Economics - - Financial Institutions and Services - - - General

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