[01] Feng Jiang, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China. [02] Wenjun Wu, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.

In this paper, we present a hybrid particle swarm optimization and support vector regression approach to predict exchange rate. This hybrid method examines the validity to optimize the parameters of penalty term and kernel function. For the experiments, the data of exchange rates (USD/CNY, EUR/CNY and CNY/JPY) are examined and optimized to be used for time series predictions with hybrid particle swarm optimization and support vector regression. Some experiments have been analyzed by using the hybrid regression model with four kernel functions including linear, radical basis, polynomial and sigmoid functions. The in-sample and out-of-sample results are compared with training ones. Empirical results show that the hybrid model has high accuracy and it is statistically effective for CNY exchange rate prediction.

Particle Swarm Optimization, Support Vector Regression, Exchange Rate

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