Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
IDEAS home Printed from https://ideas.repec.org/a/fip/fednep/00040.html
   My bibliography  Save this article

The effect of “regular and predictable” issuance on Treasury bill financing

Author

Listed:
  • Paul Glasserman
  • Amit Sirohi
  • Allen Zhang

Abstract

The mission of Treasury debt management is to meet the financing needs of the federal government at the lowest cost over time. To achieve this objective, the U.S. Treasury Department follows a principle of ?regular and predictable? issuance of Treasury securities. But how effective is such an approach in achieving least-cost financing of the government?s debt? This article explores this question by estimating the difference in financing costs between a pure cost-minimization strategy for setting the size of Treasury bill auctions and strategies that focus instead on ?smoothness? considerations?interpreted here as various forms of the regular and predictable principle. Using a mathematical optimization framework to analyze the alternative strategies, the authors find that the additional cost of including smoothness considerations, expressed as the increase in average auction yield over the cost-minimization strategy, is likely less than one basis point. The cost gap narrows further when the flexibility to use a limited number of cash management bills is added.

Suggested Citation

  • Paul Glasserman & Amit Sirohi & Allen Zhang, 2017. "The effect of “regular and predictable” issuance on Treasury bill financing," Economic Policy Review, Federal Reserve Bank of New York, issue 23-1, pages 43-56.
  • Handle: RePEc:fip:fednep:00040
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/epr/2017/epr_2017_treasury_glasserman.pdf?la=en
    File Function: Full text
    Download Restriction: no

    File URL: https://www.newyorkfed.org/research/epr/2017/epr_2017_treasury_glasserman
    File Function: Summary
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Canlin Li & Min Wei, 2013. "Term Structure Modeling with Supply Factors and the Federal Reserve's Large-Scale Asset Purchase Progarms," International Journal of Central Banking, International Journal of Central Banking, vol. 9(1), pages 3-39, March.
    2. Simon, David P., 1991. "Segmentation in the Treasury Bill Market: Evidence from Cash Management Bills," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 26(1), pages 97-108, March.
    3. Date, P. & Canepa, A. & Abdel-Jawad, M., 2011. "A mixed integer linear programming model for optimal sovereign debt issuance," European Journal of Operational Research, Elsevier, vol. 214(3), pages 749-758, November.
    4. David Bolder, 2008. "The Canadian Debt-Strategy Model," Bank of Canada Review, Bank of Canada, vol. 2008(Summer), pages 5-18.
    5. Balibek, Emre & Köksalan, Murat, 2010. "A multi-objective multi-period stochastic programming model for public debt management," European Journal of Operational Research, Elsevier, vol. 205(1), pages 205-217, August.
    6. Kenneth D. Garbade, 2007. "The emergence of \\"regular and predictable\\" as a Treasury debt management strategy," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Mar), pages 53-71.
    7. Seligman, Jason, 2006. "Does Urgency Affect Price at Market? An Analysis of U.S. Treasury Short-Term Finance," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(4), pages 989-1012, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wolswijk, Guido, 2020. "Drivers of European public debt management," Working Paper Series 2437, European Central Bank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seth Kopchak, 2014. "The absorption effect of US Treasury auctions," Review of Quantitative Finance and Accounting, Springer, vol. 43(1), pages 21-44, July.
    2. Warren B. Hrunga & Jason S. Seligman, 2015. "Responses to the Financial Crisis, Treasury Debt, and the Impact on Short-Term Money Markets," International Journal of Central Banking, International Journal of Central Banking, vol. 11(1), pages 151-190, January.
    3. Valladão, Davi M. & Veiga, Álvaro & Veiga, Geraldo, 2014. "A multistage linear stochastic programming model for optimal corporate debt management," European Journal of Operational Research, Elsevier, vol. 237(1), pages 303-311.
    4. Fleming, Michael & Nguyen, Giang & Rosenberg, Joshua, 2024. "How do Treasury dealers manage their positions?," Journal of Financial Economics, Elsevier, vol. 158(C).
    5. Kenneth D. Garbade & Matthew Rutherford, 2007. "Buybacks in Treasury cash and debt management," Staff Reports 304, Federal Reserve Bank of New York.
    6. Ethan Struby & Michael F. Connolly, 2022. "Shadow Rate Models and Monetary Policy," Working Papers 2022-03, Carleton College, Department of Economics.
    7. Athanasios Orphanides, 2021. "The Power of Central Bank Balance Sheets," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 39, pages 35-54, November.
    8. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    9. Kenneth D. Garbade, 2020. "Managing the Maturity Structure of Marketable Treasury Debt: 1953-1983," Staff Reports 936, Federal Reserve Bank of New York.
    10. Bua, Giovanna & Dunne, Peter G. & Sorbo, Jacopo, 2019. "Money Market Funds and Unconventional Monetary Policy," Research Technical Papers 7/RT/19, Central Bank of Ireland.
    11. Mr. Manmohan Singh & Mr. Peter Stella, 2012. "Money and Collateral," IMF Working Papers 2012/095, International Monetary Fund.
    12. Janice C. Eberly & James H. Stock & Jonathan H. Wright, 2020. "The Federal Reserve's Current Framework for Monetary Policy: A Review and Assessment," International Journal of Central Banking, International Journal of Central Banking, vol. 16(1), pages 5-71, February.
    13. Meixing Dai & Frédéric Dufourt & Qiao Zhang, 2013. "Large Scale Asset Purchases with segmented mortgage and corporate loan markets," Working Papers of BETA 2013-20, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    14. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    15. Corey Garriott & Sophie Lefebvre & Guillaume Nolin & Francisco Rivadeneyra & Adrian Walton, 2020. "Alternative futures for Government of Canada debt management," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 12(4), pages 659-685, January.
    16. Michele Manna & Emmanuela Bernardini & Mauro Bufano & Davide Dottori, 2013. "Modelling public debt strategies," Questioni di Economia e Finanza (Occasional Papers) 199, Bank of Italy, Economic Research and International Relations Area.
    17. Dimitri Vayanos & Jean‐Luc Vila, 2021. "A Preferred‐Habitat Model of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 89(1), pages 77-112, January.
    18. Grahame Johnson & Sharon Kozicki & Romanos Priftis & Lena Suchanek & Jonathan Witmer & Jing Yang, 2020. "Implementation and Effectiveness of Extended Monetary Policy Tools: Lessons from the Literature," Discussion Papers 2020-16, Bank of Canada.
    19. Ramaprasad Bhar & Malliaris & Mary Malliaris, 2015. "The impact of large-scale asset purchases on the S&P 500 index, long-term interest rates and unemployment," Applied Economics, Taylor & Francis Journals, vol. 47(55), pages 6010-6018, November.
    20. Murat Köksalan & Ceren Tuncer Şakar, 2016. "An interactive approach to stochastic programming-based portfolio optimization," Annals of Operations Research, Springer, vol. 245(1), pages 47-66, October.

    More about this item

    Keywords

    quadratic programming; debt management; Treasury auctions;
    All these keywords.

    JEL classification:

    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fednep:00040. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.