本篇探討在2010年4月16日滬深300股指期貨正式上市到2012年9月18日止的連續近月每日收盤日資料，進行區域內金融期貨市場連動關係的研究，試圖發現兩岸三地之股價指數期貨市場在亞太地區的金融主導地位，以作為國內外投資者在區域內的投資決策參考。 實證結果顯示，從共整合及向量誤差修正模型檢定發現，兩岸三地股指期貨具有長期均衡及短期的互動關係，因此可以視此三地為單一區域市場。在Granger因果檢定上，台股指數期貨雖無法預測恆生指數期貨，但仍明顯領先滬深300股指期貨且程度大於恆生指數期貨，或可推測兩岸因ECFA的簽訂使實體經濟的關聯性更為緊密，至於恆生指數期貨大多以金融、地產股為其主要成分，與大陸主要以實體經濟為主的金融市場，其Granger預測滬深300股指期貨的能力因此相對較弱。另由衝擊反應檢定得知恆生指數期貨為一獨立的市場，不受台灣及大陸指數期貨市場衝擊的影響；滬深300指數期貨因大陸金融市場逐漸開放，也會受到香港及台灣金融期貨市場之衝擊而產生影響；至於台股指數期貨則在兩岸三地，最易受到其他市場影響。最後由預測變異數分解檢定發現，台股指數期貨及滬深300股指期貨的波動皆易受到恆生股價指數期貨變異的影響，而恆生指數期貨在兩岸三地間之解釋能力最強，於兩岸三地間具金融主導地位。至於台股指數期貨對大陸金融期貨的影響也有突出的表現，因此若政府有心推展亞太金融中心之營運，勢必得加強區域間整合的力度，提出有利吸引外資之最政策，以增加台灣股市於國際間之競爭力。 / This study conducts analysis of regional linkage between financial futures market by examining consecutive daily closing information from April 16, 2010 (the official list date of CSI 300 index futures) to September 18, 2012. This study tries to find the financial dominance of these index futures market in the Asia Pacific region and hopefully it may be used as an investment decision reference for domestic and foreign investors. The empirical results show that from the total integration and vector error correction model tests and three places all indicate long-run equilibrium stock index futures and short-term interaction. Therefore, these three places can be viewed as a single regional market. In the Granger causality test on the TAIEX futures and Hang Seng Index futures, in spite of TAIEX futures can’t predict Hang Seng Index futures, it is significantly ahead of the CSI 300 index futures. TAIEX futures on the CSI 300 index futures even more impact than the Hang Seng Index Futures. It can explain that the ECFA has been signed and results show closely-related economy. Since the Hang Seng Index futures are mainly from financial and real estate stocks while the mainland-based financial market is mainly from the real economy, Granger predicts ability of CSI 300 index futures is relatively weak. Another test on the impulse response shows that (1) Hang Seng Index Futures is an independent market and is not affected by shocks from Taiwan and the mainland index futures markets, (2) CSI 300 index futures is affected by shocks from Hong Kong and Taiwan because of the gradually open financial markets, and (3) TAIEX futures can be seen as a potential Taiwanese dish economy because it is most vulnerable to other market influences among the three places. To sum up, the forecast variance decomposition tests show that TAIEX futures and the CSI 300 stock index futures are vulnerable to fluctuations in the Hang Seng index futures. In order words, the Hang Seng Index futures have the strongest explanatory power among the three places and shows financial dominance. The TAIEX futures also show its significant impact on the mainland China financial futures index. If the Government decides to promote the operation of the Asia-Pacific financial center and to increase competitiveness of Taiwan stock market, it will inevitably have to strengthen inter-regional integration efforts and make the most favorable policies to attract foreign investment.

本文使用Pesaran,Schuermann and Weiner(2004)提出的全球化向量自我迴歸模型(Global Vector Autoregression Model, GVAR)對房地產市場進行分析。 我們考慮 1994Q1 至 2011Q2 的資料，納入美國、中國、日本及台灣，每個國家各七個變數及一個國際外生變數，使用衝擊反應函數去衡量總體經濟變數與房市之間的連動性，以及房地產市場在國際之間的外溢效果。 本文針對美國實質房價衝擊、美國實質產出衝擊及台灣實質產出衝擊做探討，所得到的實證結果主要可歸納為三點:首先是美國房市下跌會傳遞至其它經濟面，如實質產出、通膨率、利率市場，影響會在第四季時恢復平穩，但多存在著長期影響。其二為當美國景氣衰退時，美國利率市場的反應較為迅速，中國、日本及台灣平均會落後一到兩季才會反應，且美國利率的反應幅度會較大。最後一點為跨國之間的房地產市場雖然沒有顯著的直接連動關係，但是會透過不同管道間接影響他國的經濟市場，其中一個管道可能是經由財富效果傳導至實質經濟面，造成消費需求上的衝擊，進而影響兩國的貿易平衡，另一方面則可能會影響各國央行的貨幣政策，透過金融管道對跨國間的投資產生影響。 關鍵詞:全球化向量自我迴歸模型、共整合、誤差修正模型、房地產市場、財富效果。

銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。 　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。 本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。 / The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model. 　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model. 　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.

本論文包括3篇時間序列方法在財務的應用。第一篇以門檻向量自我迴歸模型(threshold vector autoregression)分析股市訊息傳遞的不對稱效果；第二篇利用不對稱共整合模型(asymmetric cointegration)分析中國大陸股市之間長期均衡關係；第三篇根據追蹤資料共整合檢定(panel Cointegration test)檢定購買力平價說。 第一篇文章利用門檻向量自我迴歸模型分析Nasdaq股市對台灣、日本與韓國股市不對稱的訊息傳遞效果。實證結果發現，當Nasdaq市場處於下跌狀態時（壞消息狀態），Nasdaq市場干擾對亞洲股市的衝擊較大，反之，當Nasdaq市場處於上漲狀態時（好消息狀態）時，Nasdaq市場干擾對亞洲股市的衝擊較小，而在壞消息狀態時，Nasdaq指數大跌對Jasdaq指數與Kosdaq指數的衝擊效果大於Nasdaq指數大漲的效果，Nasdaq指數小跌所產生的衝擊與小漲所產生的效果具有對稱性。 第二篇文章以Enders and Siklos(2001)不對稱共整合模型探討，中國大陸上海及深圳A股與B股股價指數之間長期不對稱的均衡關係，實證結果發現，在1992年10月至2001年8月，上海A股指數與深圳A股指數之間具有不對稱共整合關係，且當上海A股處於好消息狀態（股市上漲）時，其誤差修正項的調整速度較壞消息狀態（股市下跌）之下為快，此外，上海A股指數與深圳A股指數之間其有雙向的連動關係。在B股開放之後，則是深圳股市A股與B股指數存在不對稱共整合關係，同時Granger因果關係檢定顯示深圳B股指數領先A股指數。 第三篇文章利用Pedroni(2001)追蹤資料共整合檢定，探討大麥克漢堡價格與CPI兩種不同的價格指數用於檢定購買力平價說的有效性，根據14個國家1992-1999年的追蹤資料得到的實證結果顯示，以名目匯率作為被解釋變數，則大麥克漢堡價格與CPI都是支持PPP假說，然而若以相對價格為被解釋變數，則只有大麥克漢堡價格是支持PPP假說，而以CPI為基礎的PPP假說則是無法得到支持。除此之外，本文的實證結論並不受生產力差異的影響。 關鍵字：門檻向量自我迴歸模型、不對稱共整合、追蹤資料共整合、股票市場、購買力平價說 / This dissertation includes three financial applications using time series methods. The first article investigates the asymmetric effects of information transmissions in stock markets using threshold vector autoregression model. The second article uses asymmetric cointegration to study the long-run equilibium relationships among Chinese stock markets. The third article uses panal cointegration to test purchasing-power parity (PPP). Firstly, we examines the asymmetric effects of information transmissions of Nasdaq stock market on Taiwan, Japan, and Korea stock markets by using a threshold vector autoregressive model. And also, we check whether Nasdaq stock market have different impacts on organized stock exchanges (including TAIEX, NIKKEI 225 Index, Korea Composite Index) and over-the-counter markets (including Taisdaq Index, Jasdaq Index, and Kosdaq Index) or not. The empirical results indicate that negative innovations in Nasdaq market (bad news regime) have large influence on Asia stock markets. Particularly, the positive innovations in Nasdaq market (good news regime) have small influence on Asia stock market. The large negative innovations in Nasdaq market have great influence than those of the large positive innovations on Jasdaq Index and Kosdaq Index in bad news regime. The second article uses Enders and Sikios's (2001) asymmetric cointegration model to investigate the long-run asymmetric equihbrium relationships. The empirical results find that there exits an asymmetric cointegrated relationship between Shanghai A share index and Shenzhen A share index for the period from October 1992 to August 2001. The adjustment parameters of error correction term at Shanghai A share market are larger in bad-news regime than those in good-news regime. This result reveals investors at Shanghai possess over-reaction behavior on news of stock market. Moreover, there exists a bi-directional Granger causality between Shanghai A share index and Shenzhen A share index. We find there exists an asymmetric cointegrated relationship between Shenzhen A share index and Shenzhen B share index after 19 February 2001. Furthermore, the Shenzhen B share index leads Shenzhen A share index after 19 February 2001. The third article uses Pedroni's (2001) panel cointegration test to examine the validity of PPP hypothesis by two different price indces, i.e. Big Mac prices and CPI. Our panel observations include 14 countries from 1992 to 1999. The empirical evidence indicates Big Mac PPP and CPI PPP is supposed if we use nominal exchange rate as the explanatory variable. Nevertheless, the Big Mac PPP is valid but CPI PPP not valid if we use price level as the explanatory variable. Moveover, our concludtion does not influenced by productivity bias. Keywords: threshold vector autoregression, asymmetric cointegration, panel cointegration, stock markets, purchasing-power parity

本研究之宗旨在於探究中國大陸對亞洲區域內國家的金融市場影響程度之變化。由過去的各國股市日報酬率資料間相關程度與政策改變間的影響結果，來觀察是否未來在兩岸政策更開放下會使中國大陸對台灣的影響程度上升，進而使國際間投資組合的風險分散效果下降。本研究自DataStream選取台灣、香港、中國大陸、泰國、印尼、新加坡、馬來西亞、菲律賓、日本以及美國等十國的股價指數日資料，以對數轉換為日報酬率後年化加以分析。選取時間自1991年7月15日(中國大陸上海證券交易所股價指數公開後)至2008年12月31日。本研究選用的方法為使用風險值(VaR; Value at Risk)的概念來取代傳統的標準差，衡量以該十國所分別組成的各投資組合風險值變動情形；以及由風險值所衍生出的Diversification Benefit與Incremental VaR的結果。發現到僅由亞洲區域國家內組成的投資組合風險分散效果逐漸下降；且效果並不如有納入區域外國家(如美國)的投資組合。接著本研究將Gaussian Copula模型放入VaR中以增加對極端值的捕捉能力，結果發現本研究所選用的指數加權移動平均法所求得之相關係數已可有效反應出各國之間的相依程度，即加入Copula的效果有限。另外藉由Copula所求得之相關係數顯示，台灣、香港對中國大陸之間的相依程度已逐漸上升，並開始出現超越美國之現象，其中又以2005年為上升趨勢的起點。最後本研究以向量自我迴歸模型(VARs)來驗證2005年前後中國大陸股市對其他亞洲區域國家的影響力是否存在結構性的改變；並再佐以變異數拆解之方法來觀察2005年前後各國家之間自發性衝擊對彼此之間的影響程度變化。研究結果發現，透過VARs可證明中國大陸對亞洲區域各國的影響力在2005年後轉變為顯著；僅對美國不存在此一現象。另外變異數拆解的結果也顯示各國之間的相依程度在2005年後有明顯的上升，中國大陸對各國的影響程度亦然。透過本研究之結論，在未來兩岸將簽訂金融監理備忘錄使整合關係提升的環境下，需提醒投資人整合關係的上升將使得以之為標的之投資組合風險分散效果下降，需作為投資策略之考量。 / The object of this research is to find out the trend of dependence and correlation between China and other Asian countries. Based on past information about the relationship between equity markets’ correlation and changes in policies, this research can make suggestions to the foreseeable future of Taiwan and China whose relationship will be more solid due to new policy. The data of this research are gathered from DataStream, which includes Taiwan, Hong Kong, China, Thailand, Indonesia, Singapore, Malaysia, Philippines, Japan and United States. Selected from 1991/07/15 (when the Shanghai SE Composite went public) to 2008/12/31, this research calculates the annualized daily return using natural logarithms of two consecutive daily index prices. This research uses Value at Risk (VaR) to measure the risk exposure of portfolios formed by ten countries, and extends to the use of Diversification Benefit and Incremental VaR. The results found out that the diversification effects of portfolio which includes only Asian countries are decreasing and inferior to the effects when cross region countries are included. The second study of this research is to combine Gaussian Copula Model with VaR to capture the effects of extreme values. Empirical results found out that the VaR using Exponentially Weighted Moving Average method is good enough for analyzing Asian stock markets. The correlation in Copula model suggests that the dependence between Taiwan and China had increased since 2005 and has the increasing trend which might overwhelm the dependence between Taiwan and United States. Final research is about using Vector Autoregressions Model (VARs) to testify is there exist any structural change of dependence before and after 2005, and using Variance Decomposition to observe the relationships between these ten countries. The results found out that there exist structural change in 2005, the post-2005 periods shows that for Asian countries the effect from China are significant and greater than pre-2005 periods.

本研究的目的在探討台灣貨幣市場短天期利率衍生性金融商品（30天期商業本票利率期貨、90天遠期利率協定〈以下簡稱FRA〉）對其即期利率（30天期商業本票利率、90天期商業本票利率）之『價格發現』功能。可由兩方面來檢定利率遠期協定或利率期貨市場之『價格發現』功能：（1）市場效率性：FRA、利率期貨價格可否作為未來到期日時即期利率之不偏預期；（2）FRA、利率期貨與即期利率價格間之領先-落後關係。 選取各交易日的日資料作為觀察值。在研究方法上採用ADF單根檢定、效率性檢定、向量自我相關模型（VAR）、Granger因果關係檢定、誤差正交檢定、共整合檢定、誤差修正模型。 結論結果發現，90天遠期利率協定（FRA）對90天期商業本票利率進行『價格發現』的分析，以「市場效率性檢定」的結果顯示此市場無效率，亦即無『價格發現』，可能是因為買FRA的機構投資人目的不是持有到到期，僅為判斷短期利率走勢方向，可能買個幾天欲賺取差價利潤，所以非為未來現貨價格的不偏預期；以「領先-落後關係分析」，顯示其無『價格發現』，此一結果的可能解釋是由於台灣FRA市場非集中市場公開交易，交易量尚不及現貨市場。因此市場資訊的不透明可能使遠期契約價格不如現貨價格般具代表性。 30天期商業本票利率期貨對30天期商業本票利率進行『價格發現』的分析，以「市場效率性檢定」結果顯示在到期日前適當的期間（24~36天）此市場具有效率性，即存在『價格發現』；而「領先-落後關係分析」結果則無明顯的領先落後，不具有期貨領先現貨的『價格發現』，此部分我們可能提出的解釋為：在30天期商業本票利率期貨剛推出不久，一般市場上的交易者大多是從事避險交易，鮮少進行投機行為，所以不具有短天的領先落後關係，其顯示價格發現是在考慮市場存在風險溢酬下，到期前24~36天的利率期貨價格是未來現貨價格的預期。

台灣不動產投資信託（T-REITs）自2005年發行至今已逾六年，然其市場表現仍不如發行之初所預期。過去國內已有許多研究針對T-REITs市場發展進行探討，然而目前就T-REITs與直接不動產投資市場價格表現間之相關研究尚付之闕如。有鑑於此，本研究藉由共整合與Granger因果關係檢定，檢視REITs與直接不動產市場間之關聯性，了解台灣與美國之REITs市場表現差異及其影響因素，進而作為改進T-REITs運作機制或架構之參考依據。 實證結果發現，美國之REITs與直接不動產市場之間存在共整合關係。此結果表示，長期而言，這兩者可能具有相似之風險分散效益。此外，透過Granger因果關係檢定發現REITs領先於直接不動產，乃因前者市場較具效率。另一方面，台灣之REITs與直接不動產市場之間則不具有共整合以及領先或落後關係，然直接不動產當期價格仍會受到本身與REITs之前期價格影響。 本研究進一步分析台、美兩國實證結果之差異原因如下：資料的樣本期間、REITs市場規模、存在於T-REITs市場之集中性風險以及潛在的代理問題。其中，針對T-REITs潛在代理問題，本研究藉由分析股票與T-REIT報酬率之波動性，發現T-REIT之不動產管理機構若與母集團相關者，則其市場表現較差。因此，我們得出T-REITs市場發展主要是受限於代理問題之結論。本研究成果不僅有助於改善T-REITs市場效率，亦可提供學術與實務之參考。 / The mechanism of Real Estate Investment Trusts in Taiwan (or T-REITs) was launched in 2005, however, T-REITs market did not perform as expected. What caused the limited development of T-REITs market? Current literature on the performance between T-REITs and direct real estate investment is limited. Through the cointegration and Granger causality tests, the purpose of this study is hence to explore the short-term and long-term dynamics between REITs and direct real estate markets in the U.S. and Taiwan, respectively. This study presents evidence of the cointegration relationship between REITs and direct real estate in the U.S. It implies that the diversification properties of these two assets are likely to be similar over the long horizon. According to the Granger causality test, REITs leads direct real estate due to the market information efficiency. These findings are consistent with those of previous studies. On the other hand, we find no cointegration and lead-lag relation between T-REITs and commercial real estate. Moreover, the current commercial transaction price is affected by both its and T-REIT previous price. By comparing the difference between the results of these two countries, there are several possible explanations for the different results between the U.S. and Taiwan, including difference in sample period, market capitalization, concentrated risk, and most importantly, the potential agency problem existing in T-REITs market. Finally, the underperformance of parent-related management T-REIT is verified through the volatilities of stock and T-REIT returns. Therefore, we conclude that the limited development of T-REITs is caused by the agency problem in REITs market. Results of this study may provide T-REITs market for improving its efficiency, as well as for the reference for both academics and real practices.