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Forecasting U.S. Yield Curve Using the Dynamic Nelson–Siegel Model with Random Level Shift Parameters

Deqing Luo, Tao Pang and Jiawen Xu

Economic Modelling, 2021, vol. 94, issue C, 340-350

Abstract: In this paper, we develop a new model based on the classical dynamic Nelson-Siegel model by introducing random level shift (RLS) parameters. The built-in RLS can capture cyclical fluctuations in interest rates and structural breaks induced by technological progress, financial crisis, major monetary policy interventions, etc. In addition, the model can be used to forecast future structural breaks. We apply the model to fit and forecast daily U.S. Treasury yield curves and the model outperforms other widely used models. The empirical results show that the model not only has a better in-sample fit with residuals exhibiting less persistence but also has superior out-of-sample performance. Moreover, the model performs very well especially for short-term and long-term bonds, and the performance improves as the forecasting horizon increases.

Keywords: U.S. treasury yield curves; Dynamic Nelson-Siegel model; Random level shift (RLS); Forecasting (search for similar items in EconPapers)
JEL-codes: C22 E43 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:94:y:2021:i:c:p:340-350

DOI: 10.1016/j.econmod.2020.10.015

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