Forecasting the Yield Curve for Poland

Tomasz Piotr Kostyra and Michał Rubaszek
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Tomasz Piotr Kostyra: SGH Warsaw School of Economics, Poland

Econometric Research in Finance, 2020, vol. 5, issue 2, 103-117

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.

Keywords: Yield Curve; Forecasting; Diebold-Li Model; Machine Learning (search for similar items in EconPapers)
JEL-codes: C22 C55 E43 G12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sgh:erfinj:v:5:y:2020:i:2:p:103-117

DOI: 10.2478/erfin-2020-0006

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