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, Jahrgang 35, Nr. 1, 01.01.2016, S. 86-92.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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Wegener C, Von Spreckelsen C, Basse T, Von Mettenheim HJ. Forecasting Government Bond Yields with Neural Networks Considering Cointegration. Journal of Forecasting. 2016 Jan 1;35(1):86-92. doi: 10.1002/for.2385

Bibtex

@article{a8518109c1a44d4fac11d20add95fcc7,
title = "Forecasting Government Bond Yields with Neural Networks Considering Cointegration",
abstract = "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.",
keywords = "Economics",
author = "Christoph Wegener and {Von Spreckelsen}, Christian and Tobias Basse and {Von Mettenheim}, {Hans J{\"o}rg}",
year = "2016",
month = jan,
day = "1",
doi = "10.1002/for.2385",
language = "English",
volume = "35",
pages = "86--92",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley & Sons Ltd.",
number = "1",

}

RIS

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 -

DOI