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

George Filis, Kyriakos Kentzoglanakis and Christos Floros

International Journal of Computational Economics and Econometrics, 2009, vol. 1, issue 1, 9-22

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.

Keywords: particle swarm optimisation; PSO; vector autoregressive; VAR model training; macroeconomic indicators; oil prices; stock market performance; unemployment; consumer price index; CPI. (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:1:y:2009:i:1:p:9-22

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