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Improving Overnight Loan Identification in Payments Systems

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  • MARK REMPEL

Abstract

Information on the allocation and pricing of over‐the‐counter (OTC) markets is scarce. Furfine (1999) pioneered an algorithm that provides transaction‐level data on the OTC interbank lending market. The veracity of the data identified, however, is not well established. Using permutation methods, I estimate an upper bound on the daily false positive rate of this algorithm to be between 10% and 20%. I propose refinements that reduce the bound to 10% or lower with negligible power loss. The results suggest that the inferred prices and quantities of overnight loans provide viable estimates of interbank lending market activity for Canadian data.

Suggested Citation

  • Mark Rempel, 2016. "Improving Overnight Loan Identification in Payments Systems," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 549-564, March.
    • Handle: RePEc:wly:jmoncb:v:48:y:2016:i:2-3:p:549-564
    DOI: 10.1111/jmcb.12309
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    1. Mark Rempel, 2016. "Improving Overnight Loan Identification in Payments Systems," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 549-564, March.
    2. Dennis Kuo & David R. Skeie & James Vickery & Thomas Youle, 2013. "Identifying term interbank loans from Fedwire payments data," Staff Reports 603, Federal Reserve Bank of New York.
    3. Scott Hendry & Nadja Kamhi, 2009. "Uncollateralized overnight lending in Canada," Applied Financial Economics, Taylor & Francis Journals, vol. 19(11), pages 869-880.
    4. Viral V. Acharya & Ouarda Merrouche, 2013. "Precautionary Hoarding of Liquidity and Interbank Markets: Evidence from the Subprime Crisis," Review of Finance, European Finance Association, vol. 17(1), pages 107-160.
    5. Anna Kovner & David R. Skeie, 2013. "Evaluating the quality of fed funds lending estimates produced from Fedwire payments data," Staff Reports 629, Federal Reserve Bank of New York.
    6. Gara Afonso & Anna Kovner & Antoinette Schoar, 2011. "Stressed, Not Frozen: The Federal Funds Market in the Financial Crisis," Journal of Finance, American Finance Association, vol. 66(4), pages 1109-1139, August.
    7. Jason Allen & Ali Hortaçsu & Jakub Kastl, 2011. "Analyzing Default Risk and Liquidity Demand during a Financial Crisis: The Case of Canada," Staff Working Papers 11-17, Bank of Canada.
    8. Olivier Armantier & Adam Copeland, 2012. "Assessing the quality of “Furfine-based” algorithms," Staff Reports 575, Federal Reserve Bank of New York.
    9. Lanh Tran & Ba Chu & Chunfeng Huang & Kim P. Huynh, 2014. "Adaptive permutation tests for serial independence," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 183-208, August.
    10. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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    Cited by:

    1. Mark Rempel, 2016. "Improving Overnight Loan Identification in Payments Systems," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 549-564, March.
    2. Nellie Zhang, 2015. "Changes in Payment Timing in Canada’s Large Value Transfer System," Staff Working Papers 15-20, Bank of Canada.
    3. Alexander Müller & Jan Paulick, 2026. "“The Devil is in the Details, but so is Salvation”– Different Approaches in Money Market Measurement," Journal of Financial Services Research, Springer;Western Finance Association, vol. 69(3), pages 285-317, June.
    4. Maxim Ralchenko & Adrian Walton, 2022. "Historical Data on Repurchase Agreements from the Canadian Depository for Securities," Technical Reports 121, Bank of Canada.
    5. Edoardo Rainone, 2025. "Reservation Rates in Interbank Money Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(7), pages 1931-1971, October.
    6. Edoardo Rainone, 2017. "Pairwise trading in the money market during the European sovereign debt crisis," Temi di discussione (Economic working papers) 1160, Bank of Italy, Economic Research and International Relations Area.
    7. Jason Allen & James Chapman & Federico Echenique & Matthew Shum, 2016. "Efficiency And Bargaining Power In The Interbank Loan Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 691-716, May.
    8. Narayan Bulusu, 2020. "Why Do Central Banks Make Public Announcements of Open Market Operations?," Staff Working Papers 20-35, Bank of Canada.
    9. Rainone, Edoardo, 2020. "The network nature of over-the-counter interest rates," Journal of Financial Markets, Elsevier, vol. 47(C).
    10. Nellie (Yinan) Zhang, 2019. "Estimating the demand for settlement balances in the Canadian Large Value Transfer System: How much is too much?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 52(2), pages 735-762, May.
    11. Müller, Alexander & Paulick, Jan, 2020. ""The devil is in the details, but so is salvation": Different approachesin money market measurement," Discussion Papers 66/2020, Deutsche Bundesbank.
    12. Bulusu, Narayan & Guérin, Pierre, 2019. "What drives interbank loans? Evidence from Canada," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 427-444.
    13. Bulusu, Narayan, 2024. "Disentangling the supply and announcement effects of open market operations," Journal of Financial Markets, Elsevier, vol. 67(C).
    14. Nicholas Garvin, 2018. "Identifying Repo Market Microstructure from Securities Transactions Data," RBA Research Discussion Papers rdp2018-09, Reserve Bank of Australia.

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    More about this item

    JEL classification:

    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Systems; Standards; Regimes; Government and the Monetary System
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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