African Journal of
Business Management

  • Abbreviation: Afr. J. Bus. Manage.
  • Language: English
  • ISSN: 1993-8233
  • DOI: 10.5897/AJBM
  • Start Year: 2007
  • Published Articles: 4198

Full Length Research Paper

Technical modeling exchange rate by using genetic algorithm: A case study of the Iran’s Rial against the EU Euro

Saeed Rasekhi1* and Mehdi Rostamzadeh2
  1The University of Mazandaran, Iran. 2Department of Economics, Islamic Azad University, Salmas Branch, Salmas, Iran.
Email: [email protected]

  •  Accepted: 02 November 2011
  •  Published: 28 December 2011

Abstract

 

Genetic algorithms (GAs) are computer programs that mimic the processes of biological evolution in order to solve problems and to model evolutionary systems. In this study, we apply GAs for technical models of exchange rate determination in exchange rate market. In this framework, we estimated auto regressive (AR), moving average (MA), auto regressive with moving average (ARMA) and mean reversion (MR) as technical models for the Iran’s Rial against the European Union’s (EU) Euro (Rial/Euro) using monthly data from January 1992 to December 2008. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria; R-squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Results showed that for explanation of the Iran’s Rial against the European Union’s Euro exchange rate behavior, auto regressive (AR) and auto regressive with moving average (ARMA) are better than other technical models.

 

Key words: Genetic algorithm, technical models, exchange rate, Rial/Euro.