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Forecasting the Yield Curve for Poland

Author

Listed:
  • Tomasz Piotr Kostyra

    (SGH Warsaw School of Economics, Poland)

  • Michał Rubaszek

    (SGH Warsaw School of Economics, Poland)

Abstract

This paper evaluates the accuracy of forecasts for Polish interest rates of various maturities. We apply the traditional autoregressive Diebold-Li framework as well as its extension, in which the dynamics of latent factors are explained with machine learning techniques. Our findings are fourfold. Firstly, they show that all methods have failed to predict the declining trend of interest rates. Secondly, they suggest that the dynamic affine models have not been able to systematically outperform standard univariate time series models. Thirdly, they indicate that the relative performance of the analyzed models has depended on yield maturity and forecast horizon. Finally, they demonstrate that, in comparison to the traditional time series models, machine learning techniques have not systematically improved the accuracy of forecasts.

Suggested Citation

  • 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.
    • Handle: RePEc:sgh:erfinj:v:5:y:2020:i:2:p:103-117
    DOI: 10.2478/erfin-2020-0006
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    References listed on IDEAS

    as
    1. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    2. 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.
    3. Alois Geyer & Richard Mader, 1999. "Estimation of the term structure of interest rates - A parametric approach," Working Papers 37, Oesterreichische Nationalbank (Austrian Central Bank).
    4. Zoricic, Davor & Badurina, Marko, 2013. "Nelson-Siegel Yield Curve Model Estimation And The Yield Curve Trading In The Croatian Financial Market," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 4(2), pages 113-125.
    5. 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.
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    Cited by:

    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.

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    More about this item

    Keywords

    Yield Curve; Forecasting; Diebold-Li Model; Machine Learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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