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Jacovides, Andreas (2008) Forecasting Interest Rates from the Term Structure: Support Vector Machines Vs Neural Networks. [Dissertation (University of Nottingham only)] (Unpublished)

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Abstract

Interest rate forecasting is one of the most challenging tasks in modern nance and economics.

Several studies, examining dierent factors and statistical models, have been

employed, however they often failed to beat a simple random walk. This study suggests

the use of nonlinear, nonparametric models, namely Support Vector Machines (SVM) and

Articial Neural Networks (ANN). The methodology employed uses interest rate levels

and spreads to predict the daily changes of UK six-month, one-year, three, ve and tenyear

spot rates, six months forward. Results are promising, providing evidence in support

of the term structures predictive content. Both methods are able to identify the overall

trend of future interest rate movements and perform well in terms of Root Mean Squared

Error and Mean Absolute Error outperforming, for all maturities, the simple random walk

model. Greater accuracy is achieved in terms of predicting the correct direction and by

considering longer-maturity rates. Comparison between the two methods shows that the

accuracy and generalisation performance of SVMs is superior to that of ANNs in almost

every aspect.

ii

Item Type:	Dissertation (University of Nottingham only)
Keywords:	interest rates, support vector machines, neural networks, term structure, spot rates, artificial intelligence
Depositing User:	EP, Services
Date Deposited:	25 Sep 2008
Last Modified:	02 Feb 2018 04:51
URI:	https://eprints.nottingham.ac.uk/id/eprint/22097

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