本文研究台灣貨幣需求結構改變，我們研究「變數」值(Piecewise in Variable)的結構轉折而非「時間」值(Piecewise in Time)，因為轉折點只是轉折區間的特例，所以本文建立一「變數模糊時間序列」(Fuzzy Time Series in Variable)模型來探討「變數的轉折區間」，相較於傳統時間序列研究方法如：時間序列模型、門檻轉折點模型與模糊時間序列模型等，本文所建立的變數模糊時間序列模型，所求取的股價轉折區間，不僅可改善對稱模型殘差項的非隨機現象，同時也改善了門檻轉折模型之轉折點股價指數太低的現象，並且有效地將轉折點變更為較一般化的轉折區間，足見本文所提出變數模糊時間序列模型在結構轉折的偵測上具有相對優勢，詳述如下： （一）、相較於對稱模型方面：變數模糊時間序列模型可避免對稱模型估計貨幣需求函數所產生的偏差，並且有效改善其殘差項具有非白噪音現象。 （二）、相較於門檻轉折模型方面：1.變數模糊時間序列模型較能有效驗證以下假說：貨幣需求的股價指數彈性在高股價區時較大，以及貨幣需求的所得彈性在高股價區時較小。2.變數模糊時間序列模型所求出的股價指數轉折區間水準值，對央行目前及未來貨幣政策較具實用性，3. 變數模糊時間序列模型再預測貨幣需求時，未如門檻轉折模型產生高估的偏誤。 （三）、相較於傳統模糊時間序列模型方面：變數模糊時間序列模型已改善傳統模糊時間序列模型的結構轉折區間太長之不合理現象。 （四）、相較於以「時間」為轉折的傳統時間序列模型方面：當貨幣需求函數的重要解釋變數在短時間持續發生較大幅度變化時，傳統時間序列模型可能無法診斷出結構轉變的缺失，本文的變數模糊時間序列模型可避免此一缺失。 （五）、在政策的應用上： 1. 中央銀行若未將資料，區分高低股價指數來分段估計貨幣需求函數，將使貨幣需求的所得彈性抑或是狹義貨幣需求的股價指數彈性的估計，產生頗大的偏誤。 2. 經建會在計算台灣地區的景氣對策信號中，其金融面指標同時包括有M1B貨幣供給的增加率與股價指數變動率，如此將造成在高股價指數下，股價指數上揚時高估了台灣地區的景氣狀況，而在股價指數下降時，則反之。 另外，由於台灣欠缺貨幣需求函數的重要解釋變數「所得」的月資料，以往文獻以工業生產指數等為替代變數以估計月貨幣需求函數，本文不僅證明這些方法的缺失，並提出「模糊距離權數法」來估計出月國內生產毛額資料，此一資料不僅可避免月工業生產指數等方法的三項缺失，而且在貨幣需求的估計上與預測上均有較佳的表現。 / Whether the ”money demand function” makes “structural change” happened or not ,that is crucial research for the monetary theory field. Therefore, many foreign and domestic papers have ever made studies on this. There have two major methods of study structural change. The first method is piecewise in time that is so popular and so many lecture study by it e.g. Juda and Scadding(1982), Shen(1999) ,Lin and Huang(1999),etc . Tsay(1989) had proposed a new methoed that is piecewise in variable . Distinct situation is suitable in using the two methods .We have two reasons to use the new method to study the structural change of Taiwan’s money demand function. First one is that Friedman(1988,Paul（1992）,Wu and Shea（1993）and Shen(1996) find the trade-volume of stock market or stock price are the important factors of money demand function. TSE is 12495 in February of 1990 and 2573 in October of 1990. TSE is changing so huge but all the Papers of piecewise in time can’t detect the structural change of Taiwan money demand. The second reason is that to detect the ” interval of financial variable” of structural change of Taiwan money demand is more benefit to the Central Bank than to detect the ” past time point” of structural change.　To detect the ” interval of financial variable” of structural change of Taiwan money demand is much convenient matters for monetary policy of Center Bank from now and future. Our research propose “fuzzy time series in variable” try to find the ” smoothing interval of financial variable” of structural change of money demand . Our method has two major benefits as follow: 1. Difference to TAR model: The TAR model find out the ” point of financial variable” of structural change. It seems metaphorically money demand function’s structural change suddenly. Our method find out the ” interval of financial variable” of structural change .It’s more reasonable that structural change of money demand function is gradually. 2. Difference to STAR model: So many STAR（Smooth Transition Autoregressive ）papers also find out the Gradual Transition Interval .For example: Terasvirta and Anderson(1992), Sarantis(1999) etc. But those lectures have the following point on why our method can improve it (a).STAR is piecewise in time. (b). STAR investigate structural change by just one variable AR process. But economists concern the structural change of variables. (c). The power of STAR to detect structural change is too weak. 3. We propose new summation average entropy formula that can improve the interval of structural change too longer.

在服務資源需求量會隨時間而改變的情況下，系統的服務資源供給對致力於提供高服務品質的資源提供者來說是一個重要的議題。在服務資源可以迅速的部署和解除的假設下，像是以雲端運算為基礎之服務，本研究提供了系統性的估算服務資源方法，本方法之結構是以模擬為基礎並結合了非監督式學習、顧客到達率之估計以及統計技術。首先，本研究將每一日之顧客到達率進行分群運算並將具有類似顧客到達模式的日期分為一群，且每一群之包含日期具備可解釋之代表性；下一階段使用兩階段式的忙碌因子模型去建立每一群的顧客到達率模型，並估計該群的多區間普瓦松分布來做為系統模擬隨機過程所需之參數；最後應用了等候模型理論去設計系統模擬方法，模擬出顧客在系統中到達並接受服務的隨機過程，其結果包含觀察出顧客在系統中的等待時間和排隊長度以及所需之服務資源，並提供在不同的服務策略情形下之表現。 本研究使用了一個來自電力公司客服中心之進線量資料進行本方法之實驗，展示出如何使用本方法建立一個能滿足服務水準要求的服務資源配置策略，也和該公司過去之配置策略進行比較，並提出實質上如何提升服務品質的配置策略之建議。

網際網路時代資訊接收的便利性，使得大眾容易接收到媒體所發布的媒體資訊，而這些資料具含的意見詞彙間接反應出群眾對特定主題的情緒傾向。在針對房地產的媒體當中，當特定區域的房地產市場具有良好的發展空間而成為交易熱區時，這些針對特定區域且帶含情緒的房市篇章報導或其他影響房市之相關新聞以及廣告往往會影響我們的購屋決策。 本研究將以桃園市及台中市-兩個近五年來台灣房市較為熱門的區域作為研究區域進行分析及研究，期望找出在短期時間新聞輿情及廣告和房市交易價量的相關性以及會影響該房地產市場之因素。首先蒐集桃園市及台中市的實價登錄的房地產交易資料以及廣告後，運用文字探勘分析房市整體輿情與兩都市房地產價量之關聯性，再將新聞分群後找出特徵詞，個別建立時間序列來了解各種情緒及房地產價量的共同移動性，並結合廣告投入量找出房地產市場價量以及影響因素的領先關係。並透過自建的類神經網路模型建立針對桃園市和台中市的交易量預測模型以及針對特定房市熱門區域-青埔和七期的交易量預測模型，並透過計算輸入變數的權重總和來判別新聞情緒對於房地產成交價量的影響程度。 研究首先提供了對於新聞情緒的分類包含區域經濟情緒、區域社會情緒、區域環境情緒、區域政治情緒、稅制情緒、選舉情緒。接著進行時間序列分析指出總情緒序列與成交量的時間序列相關係數都有高於70%以上，桃園市成交量與桃園市情緒的相關係數為0.73，台中市成交量與台中市情緒的相關係數為0.81，皆呈現高度正相關，顯示桃園及台中的房市交易量與情緒現存在高度相關性。在特定新聞類別當中，透過兩個城市的相關係數比對顯示稅制新聞情緒，區域環境相關情緒，區域社會相關情緒，以上三個情緒跟房市的交易量共同移動較為明顯，相關係數皆在0.5左右甚至以上，可見這些類別的新聞能夠適時反映大眾對於特定區域的房地產的看好及看壞。在此階段也透過領先指標驗證了情緒以及廣告是會領先房市交易量，桃園以及台中兩個區域都有情緒領先交易量一個月的現象。針對特定區域的交易量研究包含青埔特區及七期重劃區，也發現到兩地的交易量高峰前一至兩個月都有一波廣告的高峰。 而在類神經網路模型方面的研究結果能夠良好地預測漲跌趨勢，利用桃園資料進行訓練並以台中資料做為測試的模型在19次的漲跌中預測出17次，而將百分之七十的桃園及台中混合資料進行訓練並其餘百分之三十做為測試的模型結果也成功在14次漲跌中預測出10次，顯示模型效果預測能力良好，並透過將輸入權重加總的方式來衡量各輸入變數的影響程度，研究結果指出總情緒，稅制情緒量，區域環境情緒量與兩地房地產市場交易量最有關聯且影響最重。最後利用時間序列得知廣告高峰會領先總交易高峰一至兩個月的特性，利用從2012年10月至2016年2月的青埔特區資料及2012年10月至2013年12月的七期重劃區資料混合進行訓練並以2014年1月至2016年2月七期重劃區資料做為測試資料的模型能夠有效在兩年內預測中三次交易高峰，顯示該模型能透過預測出下一期的廣告投入量做為中介變數進而推估出交易量高峰的時間透過此模型可在未來應用於相關政策投入市場後對市場交易量的影響，也能夠快速有效的得到預測結果，而在針對特定市場我們也可以透過預測廣告以及運用廣告為交易量的領先特性來了解在近期何時會有交易量高峰，如能配合了解市場輿情脈絡，可為房屋仲介以及建商在更精確的時間點投放廣告時機點達到廣告的最大效益。

證券市場和一般商品市場本質上有所不同; 一般商品市場的需求和供給者是截然劃分 的集團; 而證券市場是一個流通市場, 其參與者既是需求者亦是供給者, 以經濟學理 論來預測分析, 解釋證券市場行為是一種主流, 而價格和數量又是經濟學領域中最主 要兩個變數; 環視現代探討證券市場行為的文章, 大部份只偏重於價格或數量單方面 之探討; 或價格與數量間單方向因果關系的研究, 由這些研究所得結論來說明證券市 場價格和數量間關系顯然不夠, 例如在探討未來價格變動時除了前期價格因素外, 尚 有數量因素會影響未來價格因素的發展。 多元時間序列分析方法系直接使用多個變數數列資料間所顯示之自我相關特性及交叉 相關特性以設定出變數間可能存在的因果關系, 而且其具有以下之優點。(1) 序列與 序列之間可能存在領先、同時、及回饋等多種關系, 藉著MARMA 模型之設定即可顯示 多個序列間基本之動態關系。(2) 聯合多個數列來建立模型亦可利用其他數列所提供 情報提高預測之準確性。(3) 介之分析(Intervention Analysis) 或季節因素之調整 都可由MARMA 模型之建之得到更精確的結果。 本文即利用多元時間序列模式(Multiple Time Series Models簡稱MARMA)分析方法, 探討證券市場價格與數量同時對數量或同時對價格的影響。

本論文運用Box-Jenkins 隨機時間序列模式與Winters 趨勢季節平滑模式，進行廿一 縣市液化石油氣需求預測，依模式之配合度、穩定度及預測能力予以評估上述兩種模 式之優缺點，并探討各模式於運用時之限制，以供企業界與學者運用此兩種模式之參 考。 本論文共壹冊，約為五萬餘字，分為八章，茲分述如下： 第一章：闡述研究之動機目的與方法。第二章；介紹Box-Jenkins 模型之理論與建立 方法。第三章：介紹指數平滑法之發展、種類及模式之建立方法。第四章：探討良好 預測模式所應具備之條件，以為評估之標準。第五章：運用Box-Jenkins 模式進行液 化石油氣需求模式之進立與預測。第六章：運用Winters 趨勢季節平滑模式從事液化 石油氣需求預測。第七章：比較前述兩章預測之結果。第八章：結論與建議。

明天股票的價格是會漲還是會跌呢? 明天到底會不會下雨? 下期樂透開獎會是哪些號碼呢? 未來不知道會發生哪些事情? 大家總是希望能夠未卜先知、洞悉未來! 可是我們要如何進行預測呢? 本文比較了不同時間序列模型的預測績效, 而且測試預測的結合是否能夠改進預測的準確度? 時間序列模型的研究在近年來非常蓬勃地發展, 所以本文簡單介紹了時間序列模型(Time series models)當中的線性AR模型、非線性TAR模型、非線性STAR模型, 以及這些模型該如何來進行在樣本外的預測。 同時本文說明了預測的結合(Combined forecast)該如何進行? 預測結合的目的是希望能夠達到截長補短的效果! 除了傳統迴歸(Regression-based)方法和變動係數(Time-varying coefficients)方法外, 本文提出了兩種非迴歸類型的預測結合方法, 績效權數(Fitness weight)和支向機(Support Vector Machine)。 其中主要的焦點放在支向機, 因為迴歸方法可能會有共線性的問題, 支向機則是沒有這個問題。 本文實證的結果顯示, 在時間序列模型方面, 非線性模型的預測能力, 在大多數的情形底下, 都不如簡單的線性AR模型; 在預測結合的方面, 支向機的績效是和迴歸方法的績效是差不多的, 這兩者都比變動係數方法的績效來得穩固, 可是如果基底模型的預測值存在共線性的問題或樣本數目過少的問題, 那麼支向機的績效是優於迴歸方法的績效。 最後, 時間序列模型的預測績效會受到資料性質的影響, 而有極大的改變, 或許我們可以考慮使用比較保險的預測策略-預測結合, 因為預測結合的預測誤差範圍是小於時間序列模型的預測誤差範圍!

本文旨在檢定台電公司現行季節電價月份劃分之合理性，並探討影響用戶別售電量與電費收入之經濟因素。為達成此目的，本文先就負載觀點與成本觀點進行群集分析，以檢定季節電價是否具統計意義之正當性；其次建立經濟計量模型，分別採用戶別之總售電量與總電費收入做為被解釋變數，運用民國88年1月至民國91年12月之月資料進行實證分析。本文建立之經濟模型有二，分別為時間序列以及複迴歸方程式模型。經檢定分析後，本文就各實證參數之經濟意涵加以闡示，最後並提出結論以及未來研究之方向。 本文透過月資料之群集分析，顯示夏月相對於非夏月之群集差異與台電公司現行季節電價夏月與非夏月之月份相一致，證實台電公司季節電價月份劃分之合理性。其次，透過ARIMA時間序列建立之短期電力需求預測模型，經實證結果顯示：電燈與電力用戶別之售電量均逐年增加，預測民國93年1月至民國99年12月，電燈用戶之年售電量平均成長率為3.33%、電力用戶為3.23%。再者，利用複迴歸模型進行實證分析之結果發現：(一)影響售電量之主要變數為溫度。惟因電燈用戶每隔兩月抄表一次，與電力用戶按月抄表之作業方式不同，故電燈用戶每月售電量係受前期(月)溫度影響，而電力用戶則受當期(月)溫度影響。(二)各用戶別之總電費收入與售電量有明顯相關，且經估算出各月售電量之電費收入彈性顯示：電燈用戶約為0.5，電力用戶約為1。由於總電費收入為總售電量與平均電價之乘積，故電燈用戶之電費收入增加1% 時，其售電量僅增加0.5%，顯示總電費的收入增加係有部分來自於平均電價的提高；換言之，就電燈用戶別而言，其電費收入增減變化之百分比除了會受到售電量增減幅度之影響外，亦反映了平均電價變化的情形。同理，對電力用戶來說，其各月售電量之電費收入彈性接近於1，表示電費收入變化1% 時，售電量亦增加1%，即電費收入之增減變化比例主要受到售電量之同向等幅變化所影響。 至於各用戶別之電費收入方面，電燈與電力兩類用戶自民國88年初至91年底四年期間均有逐年增加之趨勢，惟電力用戶之年增加幅度有隨時間遞減之現象，且歷年大抵以7-10月份較高，2月份最低。此外，影響用戶別電費收入之解釋變數中，各類用戶之售電量最為顯著，其參數值係隱示每增加一度售電量對其電費收入之影響。其中，電燈用戶之估計參數值為2.69，而電力用戶則為1.35。再者，由其電費收入之售電量彈性係數可以發現：電燈用戶約為1.2，電力用戶約為0.7，顯示電燈用戶總售電量增加1%時，總電費收入增加的幅度大於1%，而電力用戶則相反。推估電力用戶此一彈性係數較電燈用戶低之原因在於：電力用戶與電燈用戶之電價結構不同，前者係採需量電費與能量電費之兩部電價制，而後者僅包含流動電費之一部電價。最後，實證結果亦顯示電力系統之尖峰負載與負載率會影響電費收入，惟其影響幅度不大。 / A Study on Customer-by-Category Energy Sales and Power Sales Revenue Model: The Case of Taiwan Power Company Abstract The main purposes of this study are to examine the rationality of the seasonal pricing scheme defined by summer and non-summer months and to identify economic factors influencing customer-by-category energy sales and power sales revenue, utilizing the data of Taiwan Power Company (Taipower) as an empirical case. In order to achieve this objective, the cluster analysis from the perspective of load pattern and cost pattern are examined respectively to see if the seasonal pricing scheme has statistical meaning in its pattern differences in terms of summer vs. non-summer season. Second, two economic models including time-series analysis and multiple regression equations are formulated for the empirical case study. The subtotal energy sales and the subtotal power sales revenue by different type of customer categories, i.e. lighting and industrial customers, are set to be the explained variables. Data from January 1999 to December 2002 are collected for modeling simulation tests. The economic meanings and policy implications of the modeling results are elaborated on. And conclusions with directions for further research are presented. Through the cluster analysis utilizing monthly data within the time frame mentioned above, empirical research results on the grouping cluster of summer vs. non-summer months shows a consistent trend with those defined by Taipower’s present seasonal pricing scheme. Second, the empirical results of ARIMA time-series model show that the forecasted energy sales of both lighting and industrial customers will be gradually increasing through January 2004 to December 2010, and the average annual growth rate of energy sales for the lighting customer is 3.33%, and for the industrial customer is 3.23%. On the other hand, the empirical research results through the multiple regression equations show that the main factor affecting the energy sales is temperature. Due to the different time schedules for reading electricity meters between the lighting customer and the industrial customer, i.e. the time interval for reading the meter of lighting customers is every two months and for industrial customers is every month, the monthly energy sales of the lighting customer are directly related to the temperature of the previous month, while the monthly sales of the industrial customer are directly related to the temperature of the present month. In addition, for each type of customers, there is an obvious correlation between the total power sales revenue and the total energy sales. Furthermore, the estimated elasticity of the total power sales revenue versus total energy sales is about 0.5 for the lighting customer, and about 1 for the industrial customer. Since the total power sales revenue is the product of total energy sales times the average electricity price, when the total power sales revenue increases 1% with the total energy sales only increases 0.5%, it implies that the increase of total power sales revenue not just only comes from the increase of energy sales, but also partially affected by the increase of average electricity price. Similarly, for the industrial customer, when the elasticity of their monthly total power sales revenue versus total energy sales is close to 1, it implies that when the total power sales revenue increases 1%, the total energy sales also increase about 1%. In other words, the change of percentage of the total power sales revenue is mostly attributed to the variation of total energy sales, not because of the average electricity price. As for the simulation results of the total power sales revenue, those of the lighting and industrial customers are both gradually increasing between the years 1999 to 2002. However, the increasing pace of the industrial customer tended to slow down. Moreover, both types of the customers possess a similar trend that their total power sales are higher in statistical meaning for the months from July to October, and lower for February, for those above three years. Besides, among the variables affecting each type of customer’s power sales revenue, the energy sales is the most significant one, its parameter implies that whenever the total energy sales increases one unit, i.e. one kwh, it would affect the total power sales revenue by that amount equivalent to the figure of the parameter. According to the empirical results, the estimated parameter mentioned-above of the lighting customer is 2.69, and 1.35 of the industrial customer respectively. That implies one kwh unit price for the lighting customer is 2.69 N.T. dollars, and 1.35 N.T. dollars for the industrial customer. Moreover, from the elasticity of the total energy sales versus the total power sales revenue, it shows that the elasticity of the lighting customer is around 1.2, and the elasticity of the industrial customer is around 0.7. The underlining reason of the difference between the two figures could be that the electricity pricing structure of the lighting and industrial customers are quite different. The industrial customer is charged by two-part tariff including a demand charge for the capacity use and an energy charge for the kwh use. While the lighting customer is charged simply by a single rate, i.e. the energy use. Finally, the empirical results also show that the magnitude of the peak load and the load factor of the whole electricity system also affect the total power sales revenue of each type of the customer, though with much less effect.

本篇論文在結構性時間序列模型基礎下，將中國大陸29省市自治區1978-2005年實質人均GDP，拆解出其長期趨勢變動軌跡中的水準值與斜率值，對照傳統上直接利用實質所得數據，以動態縱橫資料方法進行經濟成長條件收斂假說的檢定。本文特色在於加入潛在GDP長期趨勢項的水準值和斜率值，並利用內生解釋變數落後項動態分析。除可驗證隨著時間經過，中國相對貧窮省區是否終將逐漸趕上相對富有省份所得水準外，其次，根據GDP趨勢項一階與二階條件的收斂與否，可進而確認實質GDP收斂的本質。 我們發現，實質人均GDP收斂的本質關鍵在於潛在趨勢水準收斂，潛在GDP趨勢斜率的成長率將左右區域間實質所得收斂速度。大部分樣本中，擴大的Solow模型或考慮不同經濟開放程度因素下的內生成長模型，支持條件收斂假說，而後者設算出的收斂係數明顯較為低。此外，考慮採用Arellano and Bond(1991)的the first difference GMM估計式可能存在弱工具性問題(a weak instruments problem)，以Blundell and Bond(1998)發展出的the system GMM估計式，作為探討初始所得與經濟成長收斂的關係應是較為適合的方法。 / This research examines the economic growth conditional convergence hypothesis. Using the data of 29 provinces in Mainland China between 1978 and 2005, this study applied the structural time series model to deconstruct the provinces’ real GDP per capita into two parts - the level and the slope of trend movement. The characteristics of this paper are to include the level and the slope of trend of potential GDP and to consider the lagged dependent variables into the panel data. This study intends to validate whether the income level of relatively poor provinces will gradually catch up that of the relatively affluent provinces in Mainland China eventually. In addition, this study, based on the convergence or divergence in the first-order and second-order conditions of GDP tendency, will confirm the essence of the convergence in real GDP. The findings are that the essential key of the convergence in real GDP per capita is the convergence of the potential level of GDP. The growth of potential GDP tendency slope would affect the converging speed of real income in regions. The testing results of either the augmented Solow model or the endogenous growth model which considered different economic opening degrees both support the conditional convergence hypothesis in most sample sets, while the estimated convergence coefficients of the later are significantly lower than those of the former. In addition, considering the possible weak instruments problem in the first difference GMM estimator developed by Arellano and Bond (1991), the system GMM developed by Blundell and Bond (1998) should be a more suitable way to observe the relation between initial income level and economic growth convergence.

在過去研究犯罪經濟學的理論文獻上，失業率對各犯罪類型的影響為正向關係，但在實証文獻上的研究發現，卻有愈來愈多的証據支持此二個變數間的負向或無關係。為了解釋上述正向與負向間相反的矛盾關係，本篇論文嘗試利用兩種模型(非參數與非參數模型)與兩種效果(機會效果與動機效果)來解釋此二變數間的關係，此亦是本論文主要貢獻。其中機會效果是用以解釋失業率與犯罪間的負向關係，動機效果則用以解釋正向關係。在非參數模型中，利用失業率為景氣循環的代理變數，發現失業率與竊盜間存在正向關係，此與大多實証研究相符；失業率則和妨害風化與殺人犯罪間呈現負向相關；失業率與傷害罪間則沒有明顯正負關係。研究亦顯示，不同的犯罪類型在不同的參數模型下，統計的顯著性亦有不同，而在不同年齡層(青少年與成年人)的犯罪模型則更與理論模型結論相符。 / According to the theoretical literature on criminal economics, unemployment rate tends to be positively correlated to all types of crimes. However, more and more empirical evidence suggests otherwise. In order to clarify the relationship, this study exploits both nonparametric and parametric models and considers two effects, including opportunity and motivation effects. The presence of the opportunity effect leads to be a negative correlation between unemployment rate and crimes, while the presence of the motivation effect gives a positive correlation. Under nonparametric model where unemployment rate is used as a proxy for business cycles, we only found that there is positive correlation between unemployment rate and robbery, while obscenity and homicide are found to be negatively correlated with unemployment rate. This is in line with most empirical studies. Little correlation evidence is found for unemployment and other types of crimes. Under parametric model, the study indicates that the statistical significance differs in models, and depends on crime variable used. We found more consistent results with theoretic models for the age groups (teenagers and adults).

不同股市的報酬關聯隨時間動態改變，本研究欲了解近期美國、台灣與亞太地區的中國大陸、香港、日本及韓國的報酬連動關係，並進一步觀察次級房貸風暴期間美股對這些地區的關聯改變趨勢。本論文採用灰色理論與時間序列兩種方法，實證發現次級房貸風暴發生期間，台股及亞太地區主要指數不論在報酬率或是報酬率波動性受美股影響的程度大多增強。 實證結果顯示，在風暴期間的報酬率傳導關係，亞太以韓國影響台股最顯著，美股則全面影響亞太指數；在報酬率波動性溢傳上，亞太以日本、美股以道瓊工業影響台股最強，台股則是電子類股被美股影響最重，但營建類股在與美股或是亞太指數的關聯趨勢變化卻最明顯。另外，灰關聯分析對時間序列檢定的關聯組合可以提供互補的關聯強弱關係說明，且具有相當的正確性。 / Connections between stock markets are dynamically changing, and it affects investor's transnational investment portfolio. We focus on the relationships of stock markets among the United States, Taiwan, Japan, Korea, China and Hong Kong, and eager to understand the connection tendency between Untied States and Asian-Pacific area during the subprime mortgage crisis period. The identified research methods are time series and grey theory, including Granger causality test, GARCH model and grey relational analysis. We find out the returns and volatility in Asian-Pacific stock markets were all affected increasly by U.S. market during the subprime mortgage crisis. The main empirical results are as follows: In the relationships of returns, Korea affects Taiwan mostly in the Asian-Pacific area, and U.S. market affects all the others entirely during the subprime crisis. In the relationships of volatility, Japan and Dow Jones index affects Taiwan deeply during the period; within all the Taiwan indexs, Electronic Sector Index was affected by the U.S. market mostly than others during the same period, but the connection tendency in the Construction Sector Index with other markets changes more obviously. Otherwise, grey relational analysis can provide complementary explainations as compared to time sereies in the strength of relationships, and the explainations are with plenty credibility.