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研究生： 蔡欣蓉
Tsai, Hsin-Jung
論文名稱： 寶來台灣卓越50指數基金與TAIFEX台灣加權股價指數期貨價格間之預測能力及模擬交易分析
The Predictive Power and Forward Simulation between the Polaris Taiwan Top 50 Tracker Fund and TAIFEX Taiwan Stock Index Futures Price
指導教授： 李宏志
Li, Hong-Zhi
顏盟峯
Yan, Meng-Feng
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： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 98
中文關鍵詞： 台灣加權股價指數期貨 、寶來台灣卓越50指數基金 、誤差指數異質變異數模型 、狀態轉換指數異質變異數模型 、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 、向前模擬
外文關鍵詞： TAIFEX Taiwan Stock Index Futures, Polaris Taiwan Top 50 Tracker Fund, EC-EGARCH, RS-EGARCH, EC-EGARCHGA, RS-EGARCHGA, Forward Simulation
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    • 本研究嘗試分析台灣加權股價指數與台灣加權股價指數期貨之間的動態關係，樣本為2004年至2009年的台灣加權股價指數期貨與寶來台灣卓越50指數基金每小時的交易資料。本研究分為兩個部分，首先是檢測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)、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)這四個模型在價格預測的能力，以平均絕對誤差百分比(Mean Absolute Percentage Error, MAPE)、平均絕對誤差(Mean Absolute Error, MAE)、配對檢定(paired t-test)及費雪真實檢定(Fisher Exact Test)等檢定法來評估四種模型的預測績效。之後，對誤差指數異質變異數模型與遺傳基因演算誤差指數異質變異數混合模型使用向前模擬來檢定投資人是否可以在扣除交易成本、交易稅與無風險利率的情況獲取超額報酬。

    使用配對t檢定所得到的結果為遺傳基因演算誤差指數異質變異數混合模型的預測準確度顯著勝過遺傳基因演算狀態轉換指數異質變異數混合模型，而遺傳基因演算狀態轉換指數異質變異數混合模型的預測準確度又優於狀態轉換指數異質變異數模型。在採用費雪真實檢定對預測方向性的檢測中，並無任何模型顯著優於其它模型。在向前模擬的實證結果中，不管是誤差指數異質變異數模型或遺傳基因演算誤差指數異質變異數混合模型都無法得到超額報酬，這表示市場是效率的，投資人無法透過這些交易策略得到超額報酬。

    This study wants to analyze the dynamic relationship between TAIFEX Taiwan Stock Index and Index Futures. We apply intraday hourly data of TAIFEX Taiwan Stock Index Futures and Polaris Taiwan Top 50 Tracker Fund from January 1, 2004 through December 31, 2009 in this study. This research is divided into two parts. First, the study investigates the effectiveness of various price forecasting models including EC-EGARCH model, RS-EGARCH model, EC-EGARCHGA model and RS-EGARCHGA model. Then we evaluate which chosen models perform best in predicting price by Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), paired t-test and Fisher Exact Test. Second, we use forward simulation to examine whether investors can earn excess return that deduct the transaction cost, tax, and risk free rate by EC-EGARCH model and EC-EGARCHGA model.

    After we apply paired t-test, we get an inference that EC-EGARCHGA has significantly better forecast performance than RS-EGARCHGA in precision of prediction whereas RS-EGARCHGA has more accuracy forecast than RS-EGARCH during 2004 to 2009 for both TAIFEX Taiwan Stock Index Futures and Polaris Taiwan Top 50 Tracker Fund. But there is no superior model in direction predicting between these models by Fisher exact test. The empirical result of forward simulation shows there is no excess return by using EC-EGARCH model and EC-EGARCHGA model and it implies the market is efficient.

    Chapter 1 Introduction And Motivation 1 Chapter 2 Literature Review 6 2.1 Introduction to TAIFEX Taiwan Stock Index Futures and Polaris Taiwan Top 50 Tracker Fund 6 2.2 Relative Studies about Error Correlation model and GARCH model 8 2.3 Relative Studies about Regime-Switching Model 10 2.4 Relative Studies about Genetic Algorithms 14 Chapter 3 Data and Methodology 16 3.1 Data Sources and Sample Period 16 3.2 Unit Root Test 18 3.3 Cointegration Test 20 3.4 Length of Lag 23 3.5 Error Correction Model (ECM) 23 3.6 Bivariate Exponential GARCH (EGARCH) Model 24 3.6.1 ARCH Model 24 3.6.2 GARCH Model 25 3.6.3 EGARCH Model 26 3.6.4 Error Correction Model with EGARCH 27 3.7 Conditional Mean and Variance with Markov Regime-Switching Model 29 3.8 EGARCH with Genetic Algorithms (EC-EGARCHGA) and RS-EGARCH with Genetic Algorithms (RS-EGARCHGA) 33 3.9 The Test of Performance among the Chosen Models 35 3.10 Forward Simulation 37 3.10.1 Buy and Hold Strategy in Forward Simulation 38 3.10.2 Forward Simulation in TAIFEX Taiwan Stock Index Futures 40 3.10.3 Forward Simulation in Polaris Taiwan Top 50 Tracker Fund (TTT) 43 Chapter 4 Empirical Results and Analysis 44 4.1 Stationary Test 44 4.2 Cointegration Test 45 4.3 Length of Lag 47 4.4 Bivariate EC-EGARCH Model 47 4.5 Predictive Power for TAIFEX Taiwan Stock Index Futures and TTT 48 4.5.1 Precision of Prediction 48 4.5.2 Direction Accuracy in Forecasting 50 4.6 Results of Forward Simulation 50 Chapter 5 Conclusions and Suggestions 53 5.1 Conclusions 53 5.2 Suggestions 54 References 56

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