IDEAS home Printed from https://ideas.repec.org/a/spr/jecfin/v45y2021i2d10.1007_s12197-020-09521-9.html
   My bibliography  Save this article

Empirical analysis of term structure shifts

Author

Listed:
  • Joel R. Barber

    (Florida International University)

Abstract

Principal component analysis and factor analysis of term structure movements shows that between 80 and 90% of term structure shifts can be explained by a uniform shift that is roughly parallel. In contrast, our analysis of term structure data from 1986 to 2016 reveals that only 57% of the shifts have been uniform. Twist- and butterfly-type shifts accounted for 28 and 10%, respectively, of all shifts. Remarkably, these frequency results are roughly the same for uniform and twist shifts determined on a daily, weekly, and monthly basis over the entire sample and over three subperiods. Based on historical data, an investor should expect a uniform shift in the term structure about 57% and a twist 28% of the time.

Suggested Citation

  • Joel R. Barber, 2021. "Empirical analysis of term structure shifts," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(2), pages 360-371, April.
  • Handle: RePEc:spr:jecfin:v:45:y:2021:i:2:d:10.1007_s12197-020-09521-9
    DOI: 10.1007/s12197-020-09521-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12197-020-09521-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12197-020-09521-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emrah Ahi & Vedat Akgiray & Emrah Sener, 2018. "Robust term structure estimation in developed and emerging markets," Annals of Operations Research, Springer, vol. 260(1), pages 23-49, January.
    2. Frederick R. Macaulay, 1938. "Some Theoretical Problems Suggested by the Movements of Interest Rates, Bond Yields and Stock Prices in the United States since 1856," NBER Books, National Bureau of Economic Research, Inc, number maca38-1.
    3. Joel Barber & Mark Copper, 2012. "Principal component analysis of yield curve movements," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(3), pages 750-765, July.
    4. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    5. Juneja, Januj, 2012. "Common factors, principal components analysis, and the term structure of interest rates," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 48-56.
    6. Fisher, Lawrence & Weil, Roman L, 1971. "Coping with the Risk of Interest-Rate Fluctuations: Returns to Bondholders from Naive and Optimal Strategies," The Journal of Business, University of Chicago Press, vol. 44(4), pages 408-431, October.
    7. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    8. Michael D. Bauer & James D. Hamilton, 2018. "Robust Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 399-448.
    9. Arcady Novosyolov & Daniel Satchkov, 2008. "Global term structure modelling using principal component analysis," Journal of Asset Management, Palgrave Macmillan, vol. 9(1), pages 49-60, May.
    10. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    11. Johan Hagenbjörk & Jörgen Blomvall, 2019. "Simulation and evaluation of the distribution of interest rate risk," Computational Management Science, Springer, vol. 16(1), pages 297-327, February.
    Full references (including those not matched with items on IDEAS)

    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. Liu, Yan & Wu, Jing Cynthia, 2021. "Reconstructing the yield curve," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1395-1425.
    2. Valentin Haddad & David Sraer, 2020. "The Banking View of Bond Risk Premia," Journal of Finance, American Finance Association, vol. 75(5), pages 2465-2502, October.
    3. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    4. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
    5. Galen Sher & Giuseppe Loiacono, 2013. "Maturity Transformation and Interest Rate Risk in Large European Bank Loan Portfolios," EcoMod2013 5442, EcoMod.
    6. Rajnish Mehra & Arunima Sinha, 2016. "The Term Structure of Interest Rates in India," NBER Working Papers 22020, National Bureau of Economic Research, Inc.
    7. Jorge Miguel Ventura Bravo & Carlos Manuel Pereira da Silva, 2005. "Immunization Using a Parametric Model of the Term Structure," Economics Working Papers 19_2005, University of Évora, Department of Economics (Portugal).
    8. Christos Ioannidis & Kook Ka, 2021. "Economic Policy Uncertainty and Bond Risk Premia," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(6), pages 1479-1522, September.
    9. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    10. Nguyen, Minh, 2020. "Collateral haircuts and bond yields in the European government bond markets," International Review of Financial Analysis, Elsevier, vol. 69(C).
    11. Schich, Sebastian T., 1996. "Alternative specifications of the German term structure and its information content regarding inflation," Discussion Paper Series 1: Economic Studies 1996,08e, Deutsche Bundesbank.
    12. John Y. Campbell & Robert J. Shiller & Luis M. Viceira, 2009. "Understanding Inflation-Indexed Bond Markets," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 79-138.
    13. Chamon, Marcos & Schumacher, Julian & Trebesch, Christoph, 2018. "Foreign-Law Bonds: Can They Reduce Sovereign Borrowing Costs?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 114, pages 164-179.
    14. Venetis, Ioannis & Ladas, Avgoustinos, 2022. "Co-movement and global factors in sovereign bond yields," MPRA Paper 115801, University Library of Munich, Germany.
    15. Jondeau, E. & Sedillot, F., 1998. "La prevision des taux longs français et allemands a partir d'un modele a anticipations rationnelles," Working papers 55, Banque de France.
    16. Entrop, Oliver & Fischer, Georg & McKenzie, Michael & Wilkens, Marco & Winkler, Christoph, 2016. "How does pricing affect investors’ product choice? Evidence from the market for discount certificates," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 195-215.
    17. Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
    18. Mohamed Amine Boutabba & Yves Rannou, 2020. "Investor strategies and Liquidity Premia in the European Green Bond market," Post-Print hal-02544451, HAL.
    19. Gauthier, Geneviève & Simonato, Jean-Guy, 2012. "Linearized Nelson–Siegel and Svensson models for the estimation of spot interest rates," European Journal of Operational Research, Elsevier, vol. 219(2), pages 442-451.
    20. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2010. "Inflation Expectations and Risk Premiums in an Arbitrage‐Free Model of Nominal and Real Bond Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 143-178, September.

    More about this item

    Keywords

    Term structure shift; Spot rates; Principal component analysis;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:spr:jecfin:v:45:y:2021:i:2:d:10.1007_s12197-020-09521-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.