Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery

Da Silva Fernandes, F., Stasinakis, C. and Zekaite, Z. (2019) Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery. Annals of Operations Research, 282(1-2), pp. 87-118. (doi: 10.1007/s10479-018-2808-0)

Abstract

This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: ZEKAITE, ZIVILE and Stasinakis, Professor Charalampos
• Authors: Da Silva Fernandes, F., Stasinakis, C., and Zekaite, Z.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Annals of Operations Research
• Publisher: Springer
• ISSN: 0254-5330
• ISSN (Online): 1572-9338
• Published Online: 15 March 2018
• Copyright Holders: Copyright © 2018 Springer Science+Business Media, LLC
• First Published: First published in Annals of Operations Research 282(1-2): 87-118
• Publisher Policy: Reproduced in accordance with the publisher copyright policy