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首页> 外文期刊>African Journal of Business Management >Technical modeling exchange rate by using genetic algorithm: A case study of the Irans Rial against the EU Euro
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Technical modeling exchange rate by using genetic algorithm: A case study of the Irans Rial against the EU Euro

机译:使用遗传算法的技术模型汇率:以伊朗里亚尔对欧盟欧元为例

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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.
机译:遗传算法(GA)是模拟生物进化过程的计算机程序,以解决问题并为进化系统建模。在这项研究中,我们将GA应用于汇率市场中汇率确定的技术模型。在此框架中,我们估算了自回归(AR),移动平均线(MA),带移动平均线的自动回归(ARMA)和均值回归(MR)作为伊朗里亚尔兑欧盟(EU)欧元(Rial /欧元)使用1992年1月至2008年12月的月度数据。然后,我们将这些模型放入遗传算法系统中,以测量每种模型的最佳权重。这些最佳权重已根据四个标准进行了测量: R平方(R2),均方误差(MSE),平均绝对百分比误差(MAPE)和均方根误差(RMSE)。结果表明,为解释伊朗里亚尔对欧盟的欧元汇率行为的影响,自回归(AR)和带移动平均值的自回归(ARMA)优于其他技术模型。

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