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PSO based time series models applied in exchange rate forecasting for business performance management

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Abstract

This research used the PSO algorithm to develop three new models, PSOGARCH, PSOEGARCH, and PSOGJR-GARCH, for improving business performance management. The tracking error methods are compared among the models in order to obtain a forecasting model with better performance. The three traditional time series models, GARCH, EGARCH, and GJR-GARCH, are used to undertake foreign exchange forecasting, and the results of these are compared to those of PSOGARCH, PSOEGARCH, and PSOGJR-GARCH models. The PSOGJR-GARCH model had the smallest error and the best forecasting ability, followed by the PSOEGARCH and PSOGARCH models, with the traditional GARCH models having the worst performance.

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Acknowledgments

Authors would like to thank the Global Logistics Center of National Kaohsiung University of Applied Sciences, Taiwan (ROC) for the financial support.

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Authors and Affiliations

  1. Department of International Business, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC

    Jui-Fang Chang

  2. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan, ROC

    Yueh-Min Huang

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  1. Jui-Fang Chang
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Correspondence to Jui-Fang Chang.

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Chang, JF., Huang, YM. PSO based time series models applied in exchange rate forecasting for business performance management. Electron Commer Res 14, 417–434 (2014). https://doi.org/10.1007/s10660-014-9144-5

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