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Identifying Repo Market Microstructure from Securities Transactions Data

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  • Nicholas Garvin

    (Reserve Bank of Australia)

Abstract

Interbank repo markets are arguably just as important as unsecured markets. Despite this, the global research community has not analysed the microstructure of interbank repo markets in the same detail as unsecured markets, because loan-level repo data have not been available. This paper provides and assesses an algorithm for extracting loan-level repo data from over-the-counter securities transactions data, and it applies to securities transactions data from Austraclear. This approach is similar to how loan-level unsecured data are typically obtained from payments data. False detection and false omission rates are estimated to be 3 per cent or less. While separate prudential data indicate a larger repo market than the algorithm data, likely reflecting repos transacted through foreign (i.e. non-Austraclear) infrastructure, the two datasets have a robust positive relationship. The algorithm data, capturing non-RBA repos of up to 14-days maturity from several 2-month data samples between 2006 and 2015, reveal various market features. From 2006 to 2015, the distribution of repo-rate spreads (to the cash rate) drifted up and tightened, and the market shifted towards overnight maturities. Loan-level repo rates depend on the loan size and the types of counterparties, but not how long the repo is open. In 2015, the market's network structure comprises a tightly integrated core, and a segmented periphery with few counterparties. Repo haircuts do not display obvious patterns, appearing randomly distributed around zero.

Suggested Citation

  • Nicholas Garvin, 2018. "Identifying Repo Market Microstructure from Securities Transactions Data," RBA Research Discussion Papers rdp2018-09, Reserve Bank of Australia.
  • Handle: RePEc:rba:rbardp:rdp2018-09
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    File URL: https://www.rba.gov.au/publications/rdp/2018/pdf/rdp2018-09.pdf
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    References listed on IDEAS

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    1. Adam B. Ashcraft & Darrell Duffie, 2007. "Systemic Illiquidity in the Federal Funds Market," American Economic Review, American Economic Association, vol. 97(2), pages 221-225, May.
    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. 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.
    4. Adam Ashcraft & Nicolae Gârleanu & Lasse Heje Pedersen, 2011. "Two Monetary Tools: Interest Rates and Haircuts," NBER Chapters,in: NBER Macroeconomics Annual 2010, Volume 25, pages 143-180 National Bureau of Economic Research, Inc.
    5. Leonardo Bartolini & Spence Hilton & Suresh Sundaresan & Christopher Tonetti, 2011. "Collateral Values by Asset Class: Evidence from Primary Securities Dealers," Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 248-278.
    6. 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.
    7. Olivier Armantier & Adam Copeland, 2012. "Assessing the quality of “Furfine-based” algorithms," Staff Reports 575, Federal Reserve Bank of New York.
    8. Anthony Brassil & Helen Hughson & Mark McManus, 2016. "Identifying Interbank Loans from Payments Data," RBA Research Discussion Papers rdp2016-11, Reserve Bank of Australia.
    9. 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.
    10. Gorton, Gary & Metrick, Andrew, 2012. "Securitized banking and the run on repo," Journal of Financial Economics, Elsevier, vol. 104(3), pages 425-451.
    11. Adam Copeland & Antoine Martin & Michael Walker, 2014. "Repo Runs: Evidence from the Tri-Party Repo Market," Journal of Finance, American Finance Association, vol. 69(6), pages 2343-2380, December.
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    More about this item

    Keywords

    interbank markets; Furfine algorithm; false detection rates; repo markets; securities;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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