Skip to main navigation Skip to search Skip to main content

VAR model training using particle swarm optimisation: evidence from macro-finance data

  • G. Filis
  • , Kyriakos Kentzoglanakis
  • , Christos Floros

    Research output: Contribution to journal › Article › peer-review

    Abstract

    This paper examines the empirical relationship between CPI, oil prices, stock market and unemployment in EU15 using a new computational approach. In particular, we propose a novel approach to train the well-known vector autoregressive (VAR) model using a particle swarm optimisation (PSO) method. Results demonstrate that PSO succeeds in training the model parameters. Furthermore, as the prediction error is found to be low, this strengthens the validity and usability of PSO as a model training method. The empirical results suggest that oil is an important determinant of CPI and stock market changes. Oil price changes affect CPI positively and stock market negatively. Finally, we report no evidence that CPI and unemployment have a negative effect on stock market performance.
    Original languageEnglish
    Pages (from-to)9-22
    Number of pages14
    JournalInternational Journal of Computational Economics and Econometrics
    Volume1
    Issue number1
    DOIs
    Publication statusPublished - 2009

    Fingerprint

    Dive into the research topics of 'VAR model training using particle swarm optimisation: evidence from macro-finance data'. Together they form a unique fingerprint.

    Cite this