Forecasting Government Bond Yields with Neural Networks Considering Cointegration
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Forecasting Government Bond Yields with Neural Networks Considering Cointegration. / Wegener, Christoph; Von Spreckelsen, Christian; Basse, Tobias et al.
In: Journal of Forecasting, Vol. 35, No. 1, 01.01.2016, p. 86-92.Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Forecasting Government Bond Yields with Neural Networks Considering Cointegration
AU - Wegener, Christoph
AU - Von Spreckelsen, Christian
AU - Basse, Tobias
AU - Von Mettenheim, Hans Jörg
PY - 2016/1/1
Y1 - 2016/1/1
N2 - This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research.
AB - This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research.
KW - Economics
U2 - 10.1002/for.2385
DO - 10.1002/for.2385
M3 - Journal articles
VL - 35
SP - 86
EP - 92
JO - Journal of Forecasting
JF - Journal of Forecasting
SN - 0277-6693
IS - 1
ER -