Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries

Open Access

Abstract

Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study we apply GAs for Fundamental Models of Exchange Rate Determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices, equilibrium exchange rate and portfolio balance model as fundamental models for European Union's Euro against the US Dollar 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 i.e. R-squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Based on obtained Results, it seems that for explaining of EU Euro against the US Dollar exchange rate behavior, equilibrium exchange rate and portfolio balance model are better than the other fundamental models.