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Interest Rate Forecasting with Neural Networks

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  • Jan Täppinen
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    Abstract

    This paper compares neural networks and linear regression models in interest rate forecasting using US term structure data. The expectations hypothesis gets some extra support from the neural network model as compared to the regression model. A neural network with the whole yield curve spectre from the difference between 1 and 3-month rates to the difference between 5 and 10-year rates predicts changes in interest rates quite well. However, during 1994?1995 the neural networks (as well as the regression) fails in predicting the rising interest rates.

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    File URL: http://www.vatt.fi/file/vatt_publication_pdf/k170.pdf
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    File URL: http://www.vatt.fi/publications/latestPublications/publication/Publication_1345_id/212
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    Bibliographic Info

    Paper provided by Government Institute for Economic Research Finland (VATT) in its series Discussion Papers with number 170.

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    Date of creation: 01 Jan 1998
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    Handle: RePEc:fer:dpaper:170

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    Related research

    Keywords: Expectations hypothesis; neural networks; forecasting; interest rate;

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    1. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-92.
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