Forecasting Government Bond Yields with Neural Networks Considering Cointegration. (23rd November 2015)

• Record Type: Journal Article
• Authors: Wegener, Christoph; von Spreckelsen, Christian; Basse, Tobias; von Mettenheim, Hans‐Jörg
• Is Part Of: Journal of forecasting. Volume 35:Number 1(2016)
• Page Start: 86
• Page End: 92
• Publication Date: 2015-11-23
• Subjects: neural networks -- cointegration -- government bond yields; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2385
• Languages: English
• ISSNs: 0277-6693

Abstract

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. Copyright © 2015 John Wiley & Sons, Ltd.