PSO植基於GARCH與EGARCH建構匯率預測模型

The Construction of PSO based GARCH and EGARCH Model for Forecasting Exchange Rate

10.30087/APEMR.201203.0002

張瑞芳；陳玉鎔

GARCH ； EGARCH ； 粒子群演算法 ； 匯率 ； 預測 ； GARCH ； EGARCH ； PSO ； Exchange Rate ； Forecast

亞太經濟管理評論

15卷2期（2012 / 03 / 01）

21 - 37

繁體中文

本文利用傳統時間序列模型GARCH與EGARCH，引入最佳化演算法PSO建構新模型PSOGARCH與PSOEGARCH。繼而透過追蹤誤差對匯率預測進行比較，期望建構預測能力較高的匯率預測模型。本文首先引用Chang and Tzeng（2009）挑選對匯率影響的27個變數，並以傳統時間序列模型GARCH與EGARCH進行預測，進而利用最佳化技術PSO模型篩選變數，再投入GARCH與EGARCH進行預測，最後將上述模型進行追蹤誤差比較，結果顯示PSOEGARCH模型具有最小的預測誤差，亦即顯示其預測能力最佳;其次為PSOGARCH模型；傳統時間序列GARCH模型之預測能力則為最差。

This research utilized traditional time series models GARCH and EGARCH, then introducing PSO optimization algorithm for composing new models PSOGARCH and PSOEGARCH. The tracking error methods are compared among the models for expecting to construct a higher performance forecasting model. First of all, this article refers to Chang and Tzeng (2009) selection effect on the exchange rate of 27 variables. Furthermore, traditional time series models GARCH and EGARCH are used to the foreign exchange forecasting, and then PSO model filter variables for re-entering models GARCH and EGARCH prediction. Finally, comparing for the above models, the results showed of PSOEGARCH model with the smallest error that display the best forecasting ability, second PSOGARCH model, and the traditional times series forecasting of GARCH models are the worst.

1. Cassel, G., "The Present Situation of the Foreign Exchange," Economic Journal, 26(101), 1916, pp. 62-65.
2. Balassa, B.(1964).The Purchasing-Power Parity Doctrine: A Reappraisal.Journal of Political Economy,72(6),584-596.
3. Bollerslev, T.(1986).Generalized Autoregressive Conditional Heteroscedasticity.Journal of Econometrics,31,307-327.
4. Chang, J. F.,Chang, C. W.,Tzeng, W. Y.(2009).Forecasting Exchange Rates Using Integration of Particle Swarm Optimization and Neural Networks.2009 Fourth International Conference on Innovative Computing
5. Dickey, D. A.,Fuller, W. A.(1979).Distribution of the Estimators for Autoregressive Time Series with a Unit Root.Journal of the American Statistical Association,74,427-431.
6. Engle, R. F.(1982).Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK.Econometrica,50,987-1008.
7. Engle, R. F.,Yoo, B. S.(1987).Forecasting and Testing in Cointegrated Systems.Journal of Econometrics,35,143-59.
8. Frenkle, J. A.,Bilson, J.(1970).Optimal International Reserves: A Stochastic Framework.Economic Journal,91,507-514.
9. Fuller, W. A.(1976).Introduction to Statistical Time Series.New York:John Wiley and Sons.
10. Hung, J. C.(2009).A Fuzzy Asymmetric GARCH Model Applied to Stock Markets.Information Sciences,179,3930-3943.
11. Kennedy, J.,Eberhart, R.(1995).Particle Swarm Optimization.Proceedings of the IEEE International Conference on Neural Network,Perth, Australia:
12. Krueger, David(Ed.)(1986).The Last Taboo; Money as Symbol and Reality in Psychotherapy and Psychoanalysis.New York:Brunner/Mazel.
13. Neely, C. J.,Weller, P. A.(2002).Predicting Exchange Rate Volatility: Genetic Programming versus GARCH and RiskMetrics.The Federal Reserve Bank of St. Louis
14. Nelson, D.(1991).Conditional Heteroskedasticity in Asset Returns: A New Approach.Econometrica,59,347-370.
15. Phillips, P. C. B.,Perron, P.(1988).Testing for a Unit Root in Time Series Regression.Biometrika,75,335-346.
16. Shi, Y.,Eberhart, R. C.(2001).Particle Swarm Optimization with Fuzzy Adaptive Inertia Weight.Proceedings of the Workshop on Particle Swarm Optimization,Indianapolis, IN:
17. Shi, Y.,Eberhart, R. C.(1998).Parameter Selection in Particle Swarm Optimization.Evolutionary Programming VII: Proc. EP98,New York:
18. Shi, Y.,Eberhart, R. C.(2001).Fuzzy Adaptive Particle Swarm Optimization.Proc. Congress on Evolutionary Computation 2001, Seoul, Korea,Piscataway, NJ:
19. Shi, Y.,Eberhart, R. C.(1998).A Modified Particle Swarm Optimizer.Proceedings of the IEEE International Conference on Evolutionary Computation,Piscataway, NJ:
20. Van den Bergh, F.,Engelbrecht, A. P.(2004).A Cooperative Approach to Particle Swarm Optimization.IEEE Transactions on Evolutionary Computation,8(3),225-239.