Forecasting Interest Rates from the Term Structure: Support Vector Machines Vs Neural Networks - Jacovides, Andreas (2008)
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