IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v70y2020icp635-648.html
   My bibliography  Save this article

Choosing the weighting coefficients for estimating the term structure from sovereign bonds

Author

Listed:
  • Lapshin, Victor
  • Sohatskaya, Sofia

Abstract

Estimates of the term structure of interest rates depend heavily on the quality of the market data from which it is constructed. Estimated rates can be incorrect due to observation errors and omissions in the data. The usual way to deal with the heteroskedasticity of observation errors is by introducing weights in the fitting procedure. There is currently no consensus in the literature about the choice of such weights. We introduce a non-parametric bootstrap-based method of introducing observation errors drawn from the empirical distribution into the model data, which allows us to perform a comparison test of different weighting schemes without implicitly favoring one of the contesting models – a common design flaw in comparison studies. We use government bonds of several countries to show that realistic observation errors can distort the estimated yield curve. Moreover, we show that using different weights or other modifications of accounting for observation errors in bond price data doesn’t always improve the term structure estimates, and often only worsens the situation. Based on our comparison, we advise to either use equal weights or weights proportional to the inverse duration in practical applications.

Suggested Citation

  • Lapshin, Victor & Sohatskaya, Sofia, 2020. "Choosing the weighting coefficients for estimating the term structure from sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 635-648.
  • Handle: RePEc:eee:reveco:v:70:y:2020:i:c:p:635-648
    DOI: 10.1016/j.iref.2020.08.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056020301805
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2020.08.011?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ubukata, M. & Fukushige, M., 2009. "Estimation and inference in the yield curve model with an instantaneous error term," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2938-2946.
    2. 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.
    3. Gimeno, Ricardo & Nave, Juan M., 2009. "A genetic algorithm estimation of the term structure of interest rates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2236-2250, April.
    4. Laurini, Márcio Poletti & Ohashi, Alberto, 2015. "A noisy principal component analysis for forward rate curves," European Journal of Operational Research, Elsevier, vol. 246(1), pages 140-153.
    5. Jordan, James V. & Mansi, Sattar A., 2003. "Term structure estimation from on-the-run Treasuries," Journal of Banking & Finance, Elsevier, vol. 27(8), pages 1487-1509, August.
    6. Carcano, Nicola & Dall'O, Hakim, 2011. "Alternative models for hedging yield curve risk: An empirical comparison," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2991-3000, November.
    7. Hana Hladíková & Jarmila Radová, 2012. "Term Structure Modelling by Using Nelson-Siegel Model," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2012(2), pages 36-55.
    8. Nymand-Andersen, Per, 2018. "Yield curve modelling and a conceptual framework for estimating yield curves: evidence from the European Central Bank’s yield curves," Statistics Paper Series 27, European Central Bank.
    9. David Bolder, 2006. "Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective," Staff Working Papers 06-48, Bank of Canada.
    10. Ioannides, Michalis, 2003. "A comparison of yield curve estimation techniques using UK data," Journal of Banking & Finance, Elsevier, vol. 27(1), pages 1-26, January.
    11. Fleming, Jeff & Whaley, Robert E, 1994. "The Value of Wildcard Options," Journal of Finance, American Finance Association, vol. 49(1), pages 215-236, March.
    12. Marida Bertocchi & Vittorio Moriggia & Jitka Dupačová, 2000. "Sensitivity of Bond Portfolio's Behavior with Respect to Random Movements in Yield Curve: A Simulation Study," Annals of Operations Research, Springer, vol. 99(1), pages 267-286, December.
    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. 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. Aryo Sasongko & Cynthia Afriani Utama & Buddi Wibowo & Zaäfri Ananto Husodo, 2019. "Modifying Hybrid Optimisation Algorithms to Construct Spot Term Structure of Interest Rates and Proposing a Standardised Assessment," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 957-1003, October.
    3. Blomvall, Jörgen & Hagenbjörk, Johan, 2019. "A generic framework for monetary performance attribution," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 121-133.
    4. Maciel, Leandro & Gomide, Fernando & Ballini, Rosangela, 2016. "A differential evolution algorithm for yield curve estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 129(C), pages 10-30.
    5. Leo Krippner, 2003. "Modelling the Yield Curve with Orthonomalised Laguerre Polynomials: An Intertemporally Consistent Approach with an Economic Interpretation," Working Papers in Economics 03/01, University of Waikato.
    6. Feng Guo, 2019. "Estimating yield curves of the U.S. Treasury securities: An interpolation approach," Review of Financial Economics, John Wiley & Sons, vol. 37(2), pages 297-321, April.
    7. Lorenčič Eva, 2016. "Testing the Performance of Cubic Splines and Nelson-Siegel Model for Estimating the Zero-coupon Yield Curve," Naše gospodarstvo/Our economy, Sciendo, vol. 62(2), pages 42-50, June.
    8. Leo Krippner, 2005. "An Intertemporally-Consistent and Arbitrage-Free Version of the Nelson and Siegel Class of Yield Curve Models," Working Papers in Economics 05/01, University of Waikato.
    9. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    10. Weihan Li & Jin E. Zhang & Xinfeng Ruan & Pakorn Aschakulporn, 2024. "An empirical study on the early exercise premium of American options: Evidence from OEX and XEO options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1117-1153, July.
    11. Zvi Wiener & Helena Pompushko, 2006. "The Estimation of Nominal and Real Yield Curves from Government," Bank of Israel Working Papers 2006.03, Bank of Israel.
    12. 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.
    13. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    14. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    15. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    16. Rainer Jankowitsch & Michaela Nettekoven, 2008. "Trading strategies based on term structure model residuals," The European Journal of Finance, Taylor & Francis Journals, vol. 14(4), pages 281-298.
    17. Tatyana Krivobokova & Göran Kauermann & Theofanis Archontakis, 2006. "Estimating the term structure of interest rates using penalized splines," Statistical Papers, Springer, vol. 47(3), pages 443-459, June.
    18. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    19. Caldana, Ruggero & Fusai, Gianluca & Roncoroni, Andrea, 2017. "Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market," European Journal of Operational Research, Elsevier, vol. 261(2), pages 715-734.
    20. Robert Ferstl & Alexander Weissensteiner, 2011. "Backtesting Short-Term Treasury Management Strategies Based on Multi-Stage Stochastic Programming," Palgrave Macmillan Books, in: Gautam Mitra & Katharina Schwaiger (ed.), Asset and Liability Management Handbook, chapter 19, pages 469-494, Palgrave Macmillan.

    More about this item

    Keywords

    Term structure of interest rates; Zero-coupon yield curve; Bond prices; Weights; Cross-validation;
    All these keywords.

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

    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:eee:reveco:v:70:y:2020:i:c:p:635-648. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.