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From LVTS to Lynx: Quantitative Assessment of Payment System Transition

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Listed:
  • Ajit Desai
  • Zhentong Lu
  • Hiru Rodrigo
  • Jacob Sharples
  • Phoebe Tian
  • Nellie Zhang

Abstract

Modernizing Canada’s wholesale payments system to Lynx from the Large Value Transfer System (LVTS) brings two key changes: (1) the settlement model shifts from a hybrid system that combined components of both real-time gross settlement (RTGS) and deferred net settlement (DNS) to an RTGS system; (2) the policy regarding queue usage changes from discouraging it to encouraging the adoption of the new liquidity-saving mechanism. We utilize this unique opportunity to quantitatively assess the effects of those changes on the behaviour of participants in the high-value payments system. Our analysis reveals the following: (1) At the system level, most payments are settled in a single stream with the liquidity-savings mechanism in Lynx—facilitating liquidity pooling and leading to higher efficiency than LVTS where payments were distributed in two streams. Moreover, due to Lynx’s liquidity-saving mechanism, many payments arrive earlier than those in LVTS, providing more opportunities for liquidity saving at the cost of slightly increased payment delay. (2) At the participant level, the responses are rather heterogeneous; however, our analysis suggests that liquidity efficiency is improved for several participants, and most experience slightly longer payment delays in Lynx than in LVTS.

Suggested Citation

  • Ajit Desai & Zhentong Lu & Hiru Rodrigo & Jacob Sharples & Phoebe Tian & Nellie Zhang, 2023. "From LVTS to Lynx: Quantitative Assessment of Payment System Transition," Staff Working Papers 23-24, Bank of Canada.
    • Handle: RePEc:bca:bocawp:23-24
    DOI: 10.34989/swp-2023-24
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    References listed on IDEAS

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    1. Bech, Morten L. & Garratt, Rod, 2003. "The intraday liquidity management game," Journal of Economic Theory, Elsevier, vol. 109(2), pages 198-219, April.
    2. Francisco Rivadeneyra & Nellie Zhang, 2022. "Payment Coordination and Liquidity Efficiency in the New Canadian Wholesale Payments System," Discussion Papers 2022-3, Bank of Canada.
    3. Soramäki, Kimmo & Cook, Samantha, 2013. "SinkRank: An algorithm for identifying systemically important banks in payment systems," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 7, pages 1-27.
    4. Morten Linnemann Bech & Yuuki Shimizu & Paul Wong, 2017. "The quest for speed in payments," BIS Quarterly Review, Bank for International Settlements, March.
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    Cited by:

    1. Iñaki Aldasoro & Ajit Desai, 2025. "AI agents for cash management in payment systems," BIS Working Papers 1310, Bank for International Settlements.
    2. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    3. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    4. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    5. Iñaki Aldasoro & Ajit Desai, 2025. "Money Talks: AI Agents for Cash Management in Payment Systems," Staff Working Papers 25-35, Bank of Canada.

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    JEL classification:

    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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