臺灣博碩士論文加值系統

本研究選取台灣加權股價指數、美國道瓊工業指數、S&P 500指數、NASDAQ指數、費城半導體指數從1996年1月4日至2001年12月31日之指數收盤資料，利用單根檢定、共整合檢定、誤差修正模型、因果關係分析探討台灣與美國股市是否存在明顯共整合關係及長期均衡式，並研究台美股市間是否具有投資風險分散效果，進一步以衝擊反應研究台美股市受外生變數衝擊影響時，對指數之間所造成的衝擊影響及連動關係有無產生變化，最後分析對台灣加權股價指數影響程度最大的美國指數為何。經由實證本研究歸納結論如下三點：
1.台美股價指數其整合階次均為一，且具有一個共整合向量，不過只有以應變數為費城半導體指數報酬率之誤差修正項顯著，所以台美指數間其長期均衡關係存在不穩定狀態，造成台美指數間雖然具有共整合關係，卻可能存在風險分散的效果。道瓊工業指數、費城半導體指數落後一期報酬率對台灣加權股價指數報酬率之影響呈顯著關係，也證明美國股市T－1交易日對台灣股市T交易日有顯著影響。
2.自身衝擊可解釋預測誤差變異數的比例由高至低依次為：道瓊工業指數報酬率、台灣加權股價指數報酬率、費城半導體指數報酬率、NASDAQ指數報酬率、S&P 500指數報酬率。
3.美國股市對台灣股市具有領先主導地位，且台美股市的連動關係只有單方向並非雙向，其中NASDAQ指數報酬率對台灣加權股價指數報酬率之立即性影響程度最大，而道瓊工業指數、費城半導體指數報酬率對台灣加權股價指數的衝擊，經過一個交易日後開始上升，而費城半導體指數報酬率對台灣加權股價指數衝擊期間最久，可持續影響達三個交易日。

This paper uses unit root test, cointegration test, error correction model, Granger causality test to analyze cointegraion relation and long run equilibrium between Taiwan and American stock markets from January 4, 1996 through December 31, 2001. Besides, we use impulse response function to detect the change in co-movement relationship between Taiwan and American stock markets as exogenous variables change. Finally, the strongest cointegration relation between Taiwan and American markets is examined. According to empirical findings, we assert the following:
1.There exists the first order integration and single cointegrating vector between Taiwan and American stock markets. The long run equilibrium is not stable because the only error correction term of the return of the Philadelphia Semiconductor Sector is significant. From this point of view, the risk diversified effect possible exist between Taiwan and American stock markets. The lag one periodic return of the Don Jones industrial average and the Philadelphia Semiconductor Sector have significantly impact on the return of Taiwan weighted stock index. It indirectly demonstrates that the T－1 trading day of American stock market has significantly influenced on the T trading day of Taiwan stock market.
2.The explained proportion due to its own shocks in sequence summarize from the results of variance decompositio: the return of the Don Jones industrial average, the return of the Taiwan weighted stock index, the return of the Philadelphia Semiconductor Sector, the return of the NASDAQ Composite, the return of the S&P 500.
3.American stock market leads Taiwan stock market and the co-movement relationship is one way not two way. The return of the NASDAQ Composite has the biggest instantaneous impact on Taiwan stock market. The influence of the return of the Don Jones industrial average and the Philadelphia Semiconductor Sector on Taiwan stock market have increased after one trading day. The impact on the Philadelphia Semiconductor Sector to Taiwan stock market has the longest duration which lasting for three trading days.

目錄

中文提要 ………………………………………………....i
英文提要 ……………...……………………………...…iii
誌謝 …………………………………………….………..v
目錄 …………………………………………...….……..vi
表次 …………………………………………………....viii
圖次 …………………………………………..……..…...x

第一章緒論
第一節 研究動機 …..…………………………….……...…..1
第二節 研究目的 …………..………………….………….....2
第三節 論文編排與架構 …………..………………………..3
第二章文獻回顧
第一節 國際投資與風險分散文獻探討 ………….……...…5
第二節 國際股市連動程度文獻探討 …………....…………8
第三章研究方法
第一節 單根檢定 ………..…………………………………12
第二節 共整合檢定 ………..………………………………16
第三節 誤差修正模型 …………..…………………………22
第四節 因果關係檢定 ……………..………………………23
第五節 衝擊反應函數與預測誤差變異數分解 …………..24
第四章實證結果分析
第一節 資料來源與處理 ………………………..…………28
第二節 變數整合階次檢定 ……………………..…………32
第三節 所有指數共整合關係檢定 ……………..…………33
第四節 誤差修正模型及因果關係檢定 ………..…………34
第五節 衝擊反應函數及預測誤差變異數分解分析 …..…41
第五章結論與後續研究建議
第一節 結論 ……………...…………………………………54
第二節 後續研究建議 …...…………………………………56
參考文獻 ………………………………………...………………57

表次
表4.1 變數定義 …………………………………….…..……..28
表4.2 各指數基本統計量 1996－2001 …………….….…..…30
表4.3 台美指數報酬率相關係數 ………………….……..…..32
表4.4 各指數單根檢定結果 ……………………….…..……..33
表4.5 所有指數共整合檢定結果 ………………….…...…….34
表4.6 誤差修正模型最適落後期數 ……….…….….…….….35
表4.7 應變數為道瓊工業指數報酬率
－（DLNDW）之誤差修正模型 …………………..…..36
表4.8 應變數為S&P 500指數報酬率
－（DLNS&P）之誤差修正模型 ……………………..36
表4.9 應變數為NASDAQ指數報酬率
－（DLNNQ ）之誤差修正模型 ……………………..37
表4.10 應變數為應變數為費城半導體指數報酬率
－（DLNPS）之誤差修正模型 ………………………..37
表4.11 應變數為應變數為台灣加權股價指數報酬率
－（DLNTW）之誤差修正模型 ……………………..38
表4.12 所有指數報酬率因果關係表 …...………………….….40
表4.13 各指數之預測誤差變異數分解 …………………....….42
表4.14 道瓊工業指數報酬率變動之衝擊反應分析表 ……….45
表4.15 S&P 500指數報酬率變動之衝擊反應分析表 ….…….47
表4.16 NASDAQ指數報酬率變動之衝擊反應分析表 …………48
表4.17 費城半導體指數報酬率變動之衝擊反應分析表 …….50
表4.18 台灣加權指數報酬率變動之衝擊反應分析表 ……….51

圖次
圖1.1 研究流程圖 ……………………………………………..4
圖3.1 實證架構圖 ……………………….…………...…….…27
圖4.1 台美股市交易時間差異－以台灣時間為準 .……....…29
圖4.2 台美股市走勢圖 …………………………………….…31
圖4.3 道瓊工業指數報酬率變動之衝擊反應分析圖 …...…..45
圖4.4 S&P 500指數報酬率變動之衝擊反應分析圖 ………..46
圖4.5 NASDAQ指數報酬率變動之衝擊反應分析圖 …........48
圖4.6 費城半導體指數報酬率變動之衝擊反應分析圖 ….....49
圖4.7 台灣加權股價指數報酬率變動之衝擊反應分析圖 .....51

參考文獻一、中文部分1.田峻吉（2001）， 「美國、日本、香港股市對台灣電子股指數的影響－GARCH模型之應用」，國立臺灣大學農業經濟學研究所未出版碩士論文。2.邱瑞文（2001），「北美自由貿易區與亞洲區域國家股價指數互動關係之研究」，國立高雄第一科技大學金融營運研究所未出版碩士論文。3.段光齡（2000），「美國、日本與亞洲四小龍之股市相關分析」，國立台北大學經濟學研究所未出版碩士論文。4.姚志泯（2001），「費城半導體指數與美光股價對台灣電子股的影響」，淡江大學管理科學研究所未出版碩士論文。5.徐守得（1995），「亞洲股市間共整合之實證研究」，證券市場發展季刊，第7卷第4期，pp.33-57。6.葉銀華（1991），「國際股票市場股價指數共移型態與關聯性之研究」，台灣經濟金融月刊，第27卷第10期，pp.11-19。7.萬文隆（2001），「重大事件對兩岸三地股市連動之研究─狀態空間模型及介入模式之應用」，國立台北大學企業管理研究所未出版碩士論文。8.詹凱婷（2001），「兩岸三地金融市場互動關係之探討」，淡江大學財務金融研究所未出版碩士論文。9.謝朝光（2001），「台灣與亞太各國股市間關連性與動態相關係數之研究」，國立台北大學企業管理研究所未出版碩士論文。二、英文部分1.Christofferesen, P. F. and F. X. Diebold, (2000), “How Relevant is Volatility Forecasting for Financial Risk Management?”, The Review of Economics and Statistics, 82(1), pp.12-22. 2.Chuppe, T., H. Haworth, and M. Watkins, (1989), “Global Finance：Causes, Consequences and Prospects for the Future”, Global Finance Journal, 1, pp.1-20.3.Dickey, D. and W.A. Fuller, (1979), “Distribution of the Estimators for Autoregressive Time Series With a Unit Root”, Journal of the American Statistical Association, 74, pp.427-431.4.Dickey, D. and W.A. Fuller, (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit root”, Econometrica, 49(4), pp.1057-1072. 5.Ender, W., (1995), “Applied Econometric Time Series”, John Wiley and Sons, New York.6.Engle, R.F. and B. Yoo, (1987), “Forecasting and Testing in Cointegrated Systems.”, Journal of Econometrics, 35, pp.143-159.7.Engle, R.F. and C.W.J. Granger, (1987), “Cointegration and Error Correction: Representation, Estimation and Testing”, Econometrica, 55, pp.251-276.8.Ewing, B.T., J.E. Payne, and S. Clifford, (1999), “NAFTA and North American Stock Market Linkages：An Empirical Note”, North American Journal of Economics and Finance, 10, pp.443-451. 9.Granger, C.W.J., (1969), “Investigating Causal Relation by Econometric Models and Cross-Spectral Methods”, Econometrica, 37(3), pp.424-438.10.Granger, C.W.J., (1986), “Developments in the Study of Cointegrated Economic Variables”, Oxford Bulletin of Economics and Statistics, 48, pp.213-225.11.Granger, C.W.J., (1988), “Some Recent Developments in a Concept of Causality”, Journal of Econometrics, 39, pp.199-211. 12.Ghosh, A., R. Saidi, and Keith H. Johnson, (1999), “Who Moves the Asia-Pacific Stock Markets-US or Japan? Empirical Evidence Based on the Theory of Cointegration?”, The Financial Review, 34, pp.159-170. 13.Grubel, H.G., (1968), ”Internationally Diversified Portfolios: Welfare Gains and Capital Flows, American Economic Review, 5, pp. 1299-1314.14.Huang, B.N., C.W. Yang, and John W.S. Hu, (2000), “Causality and Cointegration of Stock Market among the United States, Japan, and the South China Growth Triangle”, International Review of Financial Analysis, 9(3), 281-297.15.Jeon, B.N. and T.C. Chiang, (1991), “A System of Stock Prices in World Stock Exchange: Common Stochastic Trends for 1975－1990?”, Journal of Economics and Business, 43, pp. 329-338. 16.Johansen, S., (1988), “Statistical Analysis of Cointegration Vectors”, Journal of Economic Dynamics and Control, 12, pp.231-254.17.Johansen, S. and B. Nielson, (1993), “Asymptotics for Cointegration Rank Tests in the Presence of Intervention Dummies”, University of Copenhagen institute of mathematical statistics, Denmark.18.Johansen, S. and K. Juselius, (1990), “Maximum Likelihood Estimation and Inference on Cointegrattion with Applications to the Demand for Money”, Oxford Bulletin of Economics and Statistics, 52, pp.169-210.19.Kasa, K., (1992), “Common Stochastic Trends in International Stock Markets”, Journal of Monetary Economics, 29, pp. 95-24.20.Knif, J. and S. Pynnönen, (1999), “Local and Global Price Memory of International Stock Markets”, Journal of International Financial Markets, Institutions and Money, 9, pp.129-147. 21.Madura, J., (1998), “International Financial Management”, South-Western College Publish, Ohio.22.Masih, R. and A.M.M. Masih, (2001), “Long and Short Term Dynamic Causal Transmission amongst International Stock Markets”, Journal of International Money and Finance, 20, pp.563-587. 23.Maysami, R.C. and T.S. Koh, (2000), “A Vector Error Correction Model of the Singapore Stock Market”, International Review of Economics and Finance,9, pp.79-96. 24.Osterwald-Lenum, M., (1992), “A Note with Fractiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistic：Four Cases”, Oxford Bulletin of Economics and Statistics, 54, pp.461-472.25.Roca, E.D., E.A. Selvavathan, and W.F. Shepherd, (1998), “Are the ASEAN Equity Markets Interdependent?”, ASEAN Economic Bulletin, 15(2), pp.109-120. 26.Said, S. and D. Dickey, (1984), “Testing for a Unit Root in Autoregressive Moving Average Models with Unknown order”, Biometrica, 71, pp.599-607. 27.Sim, C., (1980), “Macroeconomics and Reality”, Econometrica, 48, pp.1-49.28.Stock, J., (1987), “Asymptotic Properties of Least-Squares Estimator of Cointegrating Vectors”, Economertrica, 55, pp.1035-1056.29.Stock, J. and M. Waston, (1988), “Testing for Common Trends”, Journal of the American Statistical Association, 83, pp.1097-1107.30.Valle, R.S., (1998), “A Cointegration Analysis of Latin American Stock Markets and the U.S”, Working Paper Series.