IDEAS home Printed from https://ideas.repec.org/a/ags/polgne/359261.html
   My bibliography  Save this article

Modelowanie krzywej dochodowości dla Polski z wykorzystaniem metody Nelsona-Siegla

Author

Listed:
  • Kostyra, Tomasz P.

Abstract

Yield curve modelling is an essential task for the governance of the modern economy and in particular for financial market participants, and hence it is an extensively researched topic. This paper presents yield curve modelling using the Nelson-Siegel approach for Poland, which was recently recognised as a developed country. Yield curve studies available for Poland are quite scarce and were conducted when Poland was still classified as a developing country. Therefore, it is worthwhile to examine the yield curve construction after three decades of economic transition. This study offers a model which, with certain assumptions, derives zero-coupon yield curves from the market prices of Treasury bonds. The simplifying assumptions reduce model development time, while delivering yield curves of higher accuracy than those commercially available.

Suggested Citation

  • Kostyra, Tomasz P., . "Modelowanie krzywej dochodowości dla Polski z wykorzystaniem metody Nelsona-Siegla," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2022(2).
  • Handle: RePEc:ags:polgne:359261
    DOI: 10.22004/ag.econ.359261
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/359261/files/Kostyra.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.359261?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
    ---><---

    References listed on IDEAS

    as
    1. Tomasz Piotr Kostyra & Michał Rubaszek, 2020. "Forecasting the Yield Curve for Poland," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 5(2), pages 103-117, December.
    2. Jozef Glova, 2010. "Matrix Theory Application in the Bootstrapping Method for the Term Structure of Interest Rates," Economic Analysis, Institute of Economic Sciences, vol. 43(1-2), pages 44-49.
    3. Michał Rubaszek, 2016. "Forecasting the Yield Curve With Macroeconomic Variables," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 1(1), pages 1-21, June.
    4. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    5. Annaert, Jan & Claes, Anouk G.P. & De Ceuster, Marc J.K. & Zhang, Hairui, 2013. "Estimating the spot rate curve using the Nelson–Siegel model," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 482-496.
    6. 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.
    7. 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.
    8. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    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. Tomasz P. Kostyra, 2022. "Yield Curve Modelling with the Nelson-Siegel Method for Poland," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 2, pages 44-56.
    2. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    3. Sang-Heon Lee, 2025. "An Alternative Approach for Determining the Time-Varying Decay Parameter of the Nelson-Siegel Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2965-2990, May.
    4. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    5. 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.
    6. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    7. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    8. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    9. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    10. Evangelos Salachas & Georgios P. Kouretas & Nikiforos T. Laopodis, 2024. "The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1018-1041, July.
    11. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    12. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
    13. Adam Traczyk, 2013. "Financial integration and the term structure of interest rates," Empirical Economics, Springer, vol. 45(3), pages 1267-1305, December.
    14. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    16. Jens H. E. Christensen & Jose A. Lopez & Paul L. Mussche, 2022. "Extrapolating Long-Maturity Bond Yields for Financial Risk Measurement," Management Science, INFORMS, vol. 68(11), pages 8286-8300, November.
    17. Shigenori Shiratsuka, 2025. "Monetary Policy Effectiveness under the Ultra‐Low Interest Rate Environment: Evidence from Yield Curve Dynamics in Japan," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(1), pages 98-121, February.
    18. Ioannis A. Venetis & Avgoustinos Ladas, 2023. "Co-movement and global factors in sovereign bond yields," Bulletin of Applied Economics, Risk Market Journals, vol. 10(2), pages 17-45.
    19. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    20. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," SIRE Discussion Papers 2015-71, Scottish Institute for Research in Economics (SIRE).

    More about this item

    Keywords

    ;

    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:ags:polgne:359261. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/irsghpl.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.