Forecasting government bond spreads with heuristic models

evidence from the Eurozone periphery

Filipa Da Silva Fernandes, Charalampos Stasinakis* (Corresponding Author), Zivile Zekaite

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.
Original languageEnglish
Pages (from-to)1-32
Number of pages32
JournalAnnals Of Operations Research
Volume282
Issue number1-2
Early online date15 Mar 2018
DOIs
Publication statusE-pub ahead of print - 15 Mar 2018

Fingerprint

Government bonds
Heuristics
Support vector regression
Euro zone
Bond spreads
Metaheuristics
Regression model
Autoregressive moving average
Ireland
Italy
Predictability
Portugal
Spain
Prediction model
Predictors
Regression method
Random walk
Greece

Keywords

  • Government bond spreads
  • Eurozone
  • Support vector regression
  • Krill herd
  • Sine–cosine algorithm

Cite this

Forecasting government bond spreads with heuristic models : evidence from the Eurozone periphery. / Fernandes, Filipa Da Silva; Stasinakis, Charalampos (Corresponding Author); Zekaite, Zivile.

In: Annals Of Operations Research, Vol. 282, No. 1-2, 11.2019, p. 1-32.

Research output: Contribution to journalArticle

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