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研究生：	鄭育燕 Cheng, Yu-Yen
論文名稱：	運用誤差指數異質變異數模型(EC-EGARCH),狀態轉換指數異質變異數模型(RS-EGARCH)及混合模型(Hybrid)在外匯現貨與期貨價格的預測能力 The analysis of prediction power and efficiency for the spot and futures exchange rate based on EC-EGARCH, Regime-Switching-EGARCH and Hybrid Model
指導教授：	顏盟峯 Yen, Meng-Feng 李宏志 Li, Hung-Chih
學位類別：	碩士 Master
系所名稱：	管理學院 - 財務金融研究所 Graduate Institute of Finance
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	88
中文關鍵詞：	遺傳基因演算誤差指數異質變異數混合模型、遺傳基因演算狀態轉換指數異質變異數混合模型、狀態轉換指數異質變異數模型、誤差指數異質變異數模型
外文關鍵詞：	EC-EGARCH, EC-EGARCHGA, Regime-Switching EGARCHGA, Regime-Switching EGARCH
相關次數：	點閱：88 下載：2
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本研究主要是比較狀態轉換指數異質變異數模型(RS-EGARCH)、誤差指數異質變異數模型(EC-EGARCH)、遺傳基因演算誤差指數異質變異數混合模型(EC-EGARCHGA)及遺傳基因演算狀態轉換指數異質變異數混合模型(RS-EGARCHGA)在價格的預測能力。在狀態轉換指數異質變異數模型中，假設不同時點的狀態會依照馬可夫鏈的行為而變動，而且狀態本身是一個不可觀察的馬可夫鏈的結果，在各個狀態下得到的參數估計值決定了波動的大小及持續性。由於每個狀態中的變異數不可能為固定數，所以必須考慮異質變異數的情況。Gray (1996)延伸了Hamilton (1989)提出的模型，假設狀態中的變異數遵循GARCH，而提出了RS-GARCH模型。但由於EGARCH 模型考慮資訊好壞消息可能會對波動性有不同(不對稱)的影響，所以本研究將使用RS-EGARCH作為預測波動性的模型。
至於誤差指數異質變異數模型，因為它同時考慮條件平均值與變異數，將會更完整地改善在價格的預測能力。此外，本研究也結合誤差指數異質變異數遺傳基因工程的混合模型來檢視是否有助於改善在價格的預測能力。這在以往文獻中，有Lai和Li(2006)發現可提高對每股盈餘的預測能力。
最後，本研究將以平均絕對誤差百分比(Mean Absolute Percentage Error, MAPE)評估在EC-EGARCH、RS-EGARCH、EC-EGARCHGA及RS-EGARCHGA四種模式中，哪一種對於外匯具有最佳預測的能力。

This study investigates the effectiveness of various price forecasting models including regime-switching EGARCH model (RS-EGARCH), Error Correction EGARCH model (EC-EGARCH), hybrid model by combining EC-EGARCH with Genetic Algorithms (EC-EGARCHGA) and hybrid model by combining RS-EGARCH with Genetic Algorithms (RS-EGARCHGA). RS-EGARCH model relies on different coefficients in each regime to account for the possibility that the financial series may undergo a finite numbers of changes over the sample period. It is unlikely that the variance will be constant within regimes and thus it’s necessary to take within-regime heteroscedasticity into consideration. Gray (1996) extends Hamilton’s (1989) model to accommodate within- regime GARCH effects with regime-switching GARCH model (RS-GARCH). In contrast to the GARCH model, the regime-switching GARCH parameters are regime-dependent. It’s an important feature because the persistence of individual shocks is lower during periods of extreme volatility. For example, Friedman and Laibson (1989) asserted that large shocks to stock market returns are not persistent, but moderate shocks are quite persistent. Since EGARCH rather than GARCH considers information asymmetry i.e. good or bad news might have different impact on volatility, regime-switching EGARCH model (RS-EGARCH) will be used in this study.
As to the EC-EGARCH model, since it considers both conditional mean and variance at the same time, it can help us predict price more completely. In addition, hybrid models, which combine EC-EGARCH with Genetic Algorithm (GA), is also used in this study to see whether artificial intelligence, GA, can help improve forecasting ability in price level since it has been supported by Lai and Li (2006) in predicting earning per share.
Finally, based on Mean Absolute Percentage Error (MAPE), we can evaluate which chosen models, EC-EGARCH, Regime-Switching EGARCH, EC-EGARCHGA and Regime-Switching EGARCHGA, performs best in predicting exchange rate.

Chapter1 Introduction.....................................1
 1.1 Purpose and Motivation...............................1
 1.2 Structure of the Research............................3
Chapter2 Literature Review................................4
 2.1 Foreign Exchange Rates is Spot and Futures...........4
  2.1.1 British Pound.....................................4
  2.1.2 Japanese Yen......................................5
  2.1.3 Swiss Franc.......................................6
 2.2 Exchange Rate Theories...............................8
  2.2.1 ARIMA.............................................8
  2.2.2 Error Correction model and GARCH..................9
  2.2.3 Relative Studies about Regime-Switching Model....10
  2.2.4 Relative Studies about Genetic Algorithms........13
Chapter3 Data and Methodology............................15
 3.1 Stationarity Test...................................15
 3.2 Cointegration Test..................................16
 3.3 Error Correction Model (ECM)........................17
 3.4 Bivariate Exponential GARCH (EGARCH) Model..........18
  3.4.1. ARCH model......................................18
  3.4.2 GARCH Model......................................19
  3.4.3 EGARCH model.....................................20
 3.5 Conditional mean and variance with Markov 
      Regime-Switching Model.............................22
 3.6 EC-EGARCH with Genetic Algorithms (EC-EGARCHGA) and
     RS-EGARCH with Genetic Algorithms (RS-EGARCHGA).....26
 3.7 The test of performance among the chosen models.....27
Chapter4 Empirical Results and Analysis..................29
 4.1 Stationarity Test...................................29
 4.2 Cointegration Test..................................29
 4.3 Bivariate EC-EGARCH Model...........................30
 4.4 Predictive Power for Spot and Futures Exchange Rate.32
  4.4.1 Precision of Prediction..........................33
Chapter5 Conclusions and Suggestions.....................52
 5.1 Conclusion..........................................52
 5-2 Suggestion..........................................53
References...............................................54
Appendix 1...............................................58
Appendix 2...............................................64
Appendix 3...............................................70
Appendix 4...............................................73
                                    

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校內：2011-06-25公開
校外：2011-06-25公開

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