Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models
AbstractThis paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics at the School of Economics and Management (ISEG), Technical University of Lisbon. in its series Working Papers with number 2006/04.
Date of creation: 2006
Date of revision:
Contact details of provider:
Postal: Department of Economics, School of Economics and Management (ISEG), Technical University of Lisbon, Rua do Quelhas 6, 1200-781 LISBON, PORTUGAL
Web page: https://aquila.iseg.utl.pt/aquila/departamentos/EC
interest rates; forecasting; neural networks; fuzzy logic.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-04-29 (All new papers)
- NEP-CBA-2006-04-29 (Central Banking)
- NEP-CMP-2006-04-29 (Computational Economics)
- NEP-FMK-2006-04-29 (Financial Markets)
- NEP-FOR-2006-04-29 (Forecasting)
- NEP-MAC-2006-04-29 (Macroeconomics)
- NEP-PBE-2006-04-29 (Public Economics)
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Vitor Escaria).
If references are entirely missing, you can add them using this form.