Forecasting the Yield Curve for Poland

T. Kostyra, Micha l Rubaszek
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引用次数: 1

Abstract

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.
预测波兰的收益率曲线
摘要本文评估了波兰不同期限利率预测的准确性。我们应用了传统的自回归Diebold Li框架及其扩展,其中用机器学习技术解释了潜在因素的动力学。我们的发现有四个方面。首先,它们表明所有的方法都无法预测利率的下降趋势。其次,他们认为动态仿射模型无法系统地优于标准的单变量时间序列模型。第三,它们表明所分析的模型的相对性能取决于收益成熟度和预测期。最后,他们证明,与传统的时间序列模型相比,机器学习技术并没有系统地提高预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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1.10
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