Forecasting the Yield Curve for Poland

  • Tomasz Piotr Kostyra SGH Warsaw School of Economics, Poland
  • Michał Rubaszek SGH Warsaw School of Economics, Poland
Keywords: Yield Curve, Forecasting, Diebold-Li Model, Machine Learning

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.

References

Breiman, Leo. 2001. “Random forests.” Machine learning 45 (1):5–32.

Christensen, Jens H. E. and Glenn D. Rudebusch. 2015. “Estimating Shadow-Rate Term Structure Models with Near-Zero Yields.” Journal of Financial Econometrics 13 (2):226–259.

Diebold, Francis X. and Canlin Li. 2006. “Forecasting the term structure of government bond yields.” Journal of Econometrics 130 (2):337–364.

Diebold, Francis X., Glenn D. Rudebusch, and Boragan S. Aruoba. 2006. “The macroeconomy and the yield curve: a dynamic latent factor approach.” Journal of Econometrics 131 (1-2):309–338.

Geyer, Alois and Richard Mader. 1999. “Estimation of the term structure of interest rates - A parametric approach.” Working Papers 37, Oesterreichische Nationalbank.

Gurkaynak, Refet S. and Jonathan H. Wright. 2012. “Macroeconomics and the Term Structure.” Journal of Economic Literature 50 (2):331–67.

Hladıkova, Hana and Jarmila Radova. 2012. “Term structure modelling by using Nelson-Siegel model.” European Financial and Accounting Journal 7 (2):36–55.

Jung, Carsten, Henrike Mueller, Simone Pedemonte, Simone Plances, and Oliver Thew. 2019. Machine learning in UK financial services. Bank of England and Financial Conduct Authority.

Marciniak, Marek. 2006. “Yield curve estimation at the National Bank of Poland.” Bank i Kredyt 10:52–74.

Martin, Daniel, Barnabas Poczos, and Burton Hollifield. 2018. “Machine learning-aided modeling of fixed income instruments.” .

Nelson, Charles R. and Andrew F. Siegel. 1987. “Parsimonious Modeling of Yield Curves.” The Journal of Business 60 (4):473–89.

Rubaszek, Micha-l. 2016. “Forecasting the yield curve with macroeconomic variables.” Econometric Research in Finance 1 (1):1–21.

Summers, Lawrence H. 2014. “U.S. Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound.” Business Economics 49 (2):65–73.

Svensson, Lars E.O. 1994. “Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994.” NBER Working Papers 4871, National Bureau of Economic Research, Inc.

Yu, William and Eric Zivot. 2010. “Forecasting the term structures of treasury and corporate yields: Dynamic nelson-siegel models evaluation.” International Journal of Forecasting, Forthcoming.

Zoricic, Davor and Marko Badurina. 2013. “Nelson-Siegel Yield Curve Model Estimation And The Yield Curve Trading In The Croatian Financial Market.” UTMS Journal of Economics 4 (2):113–125.

Published
2020-09-25
How to Cite
Kostyra, T., & Rubaszek, M. (2020). Forecasting the Yield Curve for Poland. Econometric Research in Finance, 5(2), 103 - 117. https://doi.org/10.2478/erfin-2020-0006
Section
Articles
Bookmark and Share