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Challenges in identifying interbank loans

Author

Listed:
  • Olivier Armantier
  • Adam Copeland

Abstract

Although interbank lending markets play a key role in the financial system, the lack of disaggregated data often makes the analysis of these markets difficult. To address this problem, recent academic papers focusing on unsecured loans of central bank reserves have employed an algorithm in an effort to identify individual transactions that are federal funds loans. The accuracy of the algorithm, however, is not known. The authors of this study conduct a formal test with U.S. data and find that the rate of false positives produced by one of these algorithms is on average 81 percent; the rate of false negatives is 23 percent. These results raise concerns about the information content of the algorithm's output.

Suggested Citation

  • Olivier Armantier & Adam Copeland, 2015. "Challenges in identifying interbank loans," Economic Policy Review, Federal Reserve Bank of New York, issue 21-1, pages 1-17.
  • Handle: RePEc:fip:fednep:00019
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    References listed on IDEAS

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    1. Gara Afonso & Ricardo Lagos, 2015. "Trade Dynamics in the Market for Federal Funds," Econometrica, Econometric Society, vol. 83, pages 263-313, January.
    2. Luca Arciero & Ronald Heijmans & Richard Heuver & Marco Massarenti & Cristina Picillo & Francesco Vacirca, 2016. "How to Measure the Unsecured Money Market: The Eurosystem’s Implementation and Validation Using TARGET2 Data," International Journal of Central Banking, International Journal of Central Banking, vol. 12(1), pages 247-280, March.
    3. Acharya, Viral V. & Skeie, David, 2011. "A model of liquidity hoarding and term premia in inter-bank markets," Journal of Monetary Economics, Elsevier, vol. 58(5), pages 436-447.
    4. 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, pages 691-716, May.
    5. Hamilton, James D, 1996. "The Daily Market for Federal Funds," Journal of Political Economy, University of Chicago Press, vol. 104(1), pages 26-56, February.
    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. Bech, Morten L. & Atalay, Enghin, 2010. "The topology of the federal funds market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5223-5246.
    8. Adam Copeland & Antoine Martin & Michael Walker, 2010. "The tri-party repo market before the 2010 reforms," Staff Reports 477, Federal Reserve Bank of New York.
    9. Olivier Armantier & Jeffrey Arnold & James J. McAndrews, 2008. "Changes in the timing distribution of Fedwire funds transfers," Economic Policy Review, Federal Reserve Bank of New York, vol. 14(Sep), pages 83-112.
    10. 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.
    11. Scott Hendry & Nadja Kamhi, 2007. "Uncollateralized Overnight Loans Settled in LVTS," Staff Working Papers 07-11, Bank of Canada.
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    Cited by:

    1. Huberto M. Ennis, 2019. "Interventions in Markets with Adverse Selection: Implications for Discount Window Stigma," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 1737-1764, October.
    2. Armantier, Olivier & Ghysels, Eric & Sarkar, Asani & Shrader, Jeffrey, 2015. "Discount window stigma during the 2007–2008 financial crisis," Journal of Financial Economics, Elsevier, vol. 118(2), pages 317-335.
    3. Anthony Brassil & Helen Hughson & Mark McManus, 2016. "Identifying Interbank Loans from Payments Data," RBA Research Discussion Papers rdp2016-11, Reserve Bank of Australia.

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    More about this item

    Keywords

    data quality; federal funds market;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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