Interest Rate Forecasting with Neural Networks
AbstractThis 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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Bibliographic InfoPaper provided by Government Institute for Economic Research Finland (VATT) in its series Discussion Papers with number 170.
Date of creation: 01 Jan 1998
Date of revision:
Find related papers by JEL classification:
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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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