Forecasting the Yield Curve with Dynamic Factors

Erhard Reschenhofer () and Thomas Stark
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Erhard Reschenhofer: University of Vienna, Department of Statistics and Operations Research, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria.
Thomas Stark: University of Vienna, Department of Statistics and Operations Research, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria

Journal for Economic Forecasting, 2019, issue 1, 101-113

Abstract: Using two monthly yield datasets over the periods 1970-2000 and 1990-2019, respectively, we re-examine previous findings that yield forecasts based on AR models for the dynamic factors obtained from the Nelson-Siegel curve outperform the random walk forecast and other competitors. Our empirical results do not support these findings. Only the forecasts based on AR models for the differenced yields outperform the random walk forecast. In general, the 1-month-ahead forecasts based on the dynamic factors come out worse than those based on the yields. In the case of 12-months-ahead forecasting, all forecasts perform poorly, particularly those based on AR models fitted to undifferenced time series. Seemingly more positive results obtained in previous studies are explained by a focus on a too short evaluation period.

Keywords: Nelson-Siegel curve; term structure; dynamic factors; out-of-sample forecasting; random walk benchmark; long-term forecasting (search for similar items in EconPapers)
JEL-codes: C22 E43 (search for similar items in EconPapers)
Date: 2019
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