論文提要內容：本文將應用五種不同各具代表性的時序模型：向量自我迴歸模型(VAR)、誤差修正模型(ECM)、一般自我迴歸條件異質非對稱變異數模型(EGARCH)、卡爾曼濾嘴模型(KMF)以及馬可夫狀態轉換模型(MKS)，分別就美國、英國、德國與日本等四個工業大國的股價指數作跨市場不同預測模型的交叉績效比較。
在本文研究的實證結果中可得知：在德國市場預測方面，馬可夫轉換模型在短期間內能較其餘時序模型準確的捕捉到股價報酬率的脈動，而在長期的水準下，ECM模型有相對較優異的表現。在英國市場上，在前兩個月較短期的預測水平中，線性模型似乎能有效的掌握指數報酬率短期的波動方向。而在之後較長期的預測水平中，非線性模型往往都有較佳的表現。另外其整體橫斷面的平均績效水準則以卡爾曼模型較佳。在日本市場中，短期預測水平中的最佳模型都擁有模型誤差變異非一致性或非對稱性的假設，而在長期預測水平下的最佳模型，其都相繼包含了模型動態調整的特性，另外其整體橫斷面的平均績效水準則以ECM模型較佳。最後在美國市場預測方面產生了較明顯不同的績效衡量結果，，MAD法中認定以聯立式向量自我迴歸方法所架構出模型方程式能比其他單一迴歸式的非線性模型有較佳的表現。在RMSE法中則認為市場裡長短期股性的趨勢又朝向大部分由：含有模型動態調整特性的預測方法會有較佳的預測結果，另外其整體橫斷面的平均績效水準則以EGARCH模型較佳。對於EGARCH模型未能有較為優異的預測績效，其可能原因為受到樣本數大小的影響及無法像股價報酬率波動幅度較明顯之日資料那樣擁有較強烈的資料特性所致。

Abstract：
Many empirical studies and researches believe that he overall performance of a country’s economy is strongly related to the performance of its stock market. Some empirical studies, however, show that this relationship does not necessarily hold. In other word, sometimes the stock index is viewed as random walk process. Hence, in this research we want to know that whether the stock index is predictable or not? In this research we applied five time series models to investigate on forecasting of the stock index return in American、British、German、Japanese capital markets. The analysis techniques are distinguished in two parts, which are known as the simultaneous equation model and nonlinear unique equation models. Models of the first parts compose of Vector Autoregressive Models (VAR) and Error Correction Models (ECM). The second part of the models are EGARCH、Kalman Filter Model (KFM) and Markov Switch Models (MKS) which are used to account for time-varying structure parameters and conditional variances. The forecasting performance is measure and compared with MAD and RMSE. Hence, in this research we will compare these five models and determine which of these models make the most accurate prediction.
The conclusions are shown as the following: 1) In the short-term, the MKS model capture a better rate of return on the German stock price prediction. Conversely, in the long-term market performance, the ECM model possesses a better result. 2) In the British market performance, one can see that linear equation models perform better in forecasting in the first two months. Nevertheless, nonlinear unique equations tend to carry out an excellent prediction in a long-term. The overall market performance is best forecasted by using the KFM model. 3) In the short-term Japanese market performance, the better predictable model almost have the same character about having asymmetry variance assumption. Then ECM best model for the overall performance prediction. Lastly, in the US market forecast, it is noteworthy that different forecasting models generate different effects. However, EGARCH is the best-chosen model use for market performance prediction.

目錄
第一章緒論…………………………………………………………………………1
第一節研究背景與動機……………………………………………….…….1
第二節研究目的……………………………………………..………………2
第三節研究架構………………………………………………………………5
第二章文獻探討與變數選取………………………………………………..…..7
第一節國內外文獻回顧…………………………………..………………..7
第二節總體經濟變數的選取與介紹……………………………………….16
第三章 研究方法與資料處理…………………………………………..……….23
第一節單根檢定……………………………….……….………………….23
第二節向量自我迴歸模型…………………………….……….…………..27
第三節誤差修正模型……………………………………………………….29
第四節一般化自我迴歸條件異質變異數模型……………………………..32
第五節狀態空間模型之卡爾曼濾嘴模型………………………………….37
第六節多重狀態馬可夫轉換模型的定義…………………………………..44
第七節資料來源與處理…………………………………………………….48
第八節評量預測模型能力方法……………………………………………..51
第四章 實證結果…………………………………………….……………………53
第一節單根檢定………………………………………………….…………53
第二節風險變數的取捨……………………………………………….……59
第三節最適落差期選取……………….……………………………………62
第四節向量自我迴歸模型實證研究………………………………….……64
第五節誤差修正模型實證研究……………………………………………..68
第六節EGARCH模型實證研究……………………………………………….73
第七節卡爾曼濾嘴模型實證研究………………………………………….77
第八節馬可夫轉換模型實證研究…………………………………………..82
第九節股價指數預測模型的績效分析與圖形結果………………………..91
第五章結論…………………………………………………………………108
第一節研究結論…………………… ……………..………..…….……108
第二節後續研究與建議……………… ………………………………….110
參考書目……………………………………………………………………………111

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