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Forecasting Government Bond Yields with Neural Networks Considering Cointegration

Author

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  • Christoph Wegener
  • Christian Spreckelsen
  • Tobias Basse
  • Hans‐Jörg Mettenheim

Abstract

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Suggested Citation

  • Christoph Wegener & Christian Spreckelsen & Tobias Basse & Hans‐Jörg Mettenheim, 2016. "Forecasting Government Bond Yields with Neural Networks Considering Cointegration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 86-92, January.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:1:p:86-92
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    Cited by:

    1. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    2. Kunze, Frederik & Wegener, Christoph & Bizer, Kilian & Spiwoks, Markus, 2017. "Forecasting European interest rates in times of financial crisis – What insights do we get from international survey forecasts?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 192-205.
    3. Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    4. Zhidan Luo & Wei Guo & Qingfu Liu & Yiuman Tse, 2023. "A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1138-1149, August.
    5. Nikolas Stege & Christoph Wegener & Tobias Basse & Frederik Kunze, 2021. "Mapping swap rate projections on bond yields considering cointegration: an example for the use of neural networks in stress testing exercises," Annals of Operations Research, Springer, vol. 297(1), pages 309-321, February.

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