觀光事業素有無煙囪工業之稱，自政府於民國四十八年全力推動發展以來 ，來華觀光旅客人數即不斷地成長，此對促進國民外交與增加政府的外匯 收入上有莫大的幫助。觀光旅客人數的多寡，直接影響本地觀光業者與政 府相關單位對觀光業軟硬體設施的投資，像是觀光旅館的興建、導遊人員 的培訓以及整體策略的規劃。不當的評估或不正確的需求預測，都將導致 大量觀光資源的閒置或浪費。本研究計劃主要應用簡算法、時間趨勢模式 、時間序列模式、計量經濟模式，尋找並建立來華觀光旅客長短期需求預 測模式，並針對總體或各主要市場的需求，利用各種模式評估準則提出一 最佳預測模式，以供政府相關單位與觀光業者作為往後政策釐定以及投資 計劃擬定時的參考。

動態資料往往隨著時間區間取法或測量工具的不同而有差異,此種不確定的特質我們稱為模糊性。但是傳統的時間數列仍是以確定的觀察值來記錄具有模糊性的動態資料。為了更完整的表示一個動態過程,我們考慮模糊時間數列(fuzzy time series)以具有不確定性的模糊集合來取代明確的數值,保持原來的模糊性。 本文探討模糊時間數列中模糊自我迴歸模式(fuzzy autoregressive model簡寫為 FAR 模式)的建構過程,並分別利用此模式來預測中央政府總預算和匯率。FAR 模式乃根據Box-Jenkins(1970)所提出的 ARMA 三階段模式建立的流程並推廣Zadeh(1965)所提出的模糊集合理論而來。在這過程中 ,我們考慮人類思維方法,使FAR 模式更具有彈性且適合未來預測時的需要。而對於所討論的動態過程,也不需要任何模式上的假設(例如:線性或穩定 ),因此 FAR 模式的適用範圍極為廣泛,更不會因為模式的誤判而導致預測時的嚴重錯誤。最後,我們將 FAR 模式的預測結果與傳統 ARMA 模式做比較。 文中關於模糊時間數列的一些性質,例如:模糊趨勢(fuzzy trend)和模糊穩定(fuzzy stationary),由於傳統文獻中沒有加以討論,本文亦提出定義和新的看法。 / Representations of dynamic data are always different as the time interval or measuring tool change. We call these characteristics of uncertainty fuzziness. But traditional time series use crisp observations to record a fuzzy dynamic process. To completely represent, we consider fuzzy time series replacing the crisp numbers with fuzzy sets and preserve original fuzziness. In this paper, the fuzzy autoregressive model (FAR model) of fuzzy time series is studied and used to forecast the Central government expenditure and exchange rates, respectively. The modeling process is according to Box- Jenkins' (1970) method of ARMA model and merged with the fuzzy set theory proposed by Zadeh (1965). Reasonable human judgements and ways of thinking are taken into consideration throughout the modeling process to make the FAR model more elastic and appropriate for forecasting. Unlike certain incorrectly identified models which lead to inaccurate forecasts, the FAR model can be widely applied due to its not having any assumptions on the original time series (e.g., linearity and stationarity). Finally, the performances of the FAR model to Central government expenditure and exchange rates are compared with that of the traditional ARMA model. Additionally, some properties about fuzzy time series, e.g., fuzzy trend and fuzzy stationary, have not been studied in the literature, and we propose definitions and new opinions.

自民國69年政府開放國人出國觀光之後，由於國民所得的提高、台幣的升 值及其它種種社經有利因素的影響，使得每年出國觀光人數穩定的成長， 而在民國76年開放國人赴大陸探親之後，出國觀光人數更呈直線上升，這 對於提高國家知名度以及展示國家整體經濟實力有極為明顯的助益。出國 觀光旅客人數的多寡直接或間接影響本地觀光業者及政府相關單位對觀光 業軟、硬體設施的投資以及整體策略的規劃，舉凡國際航線的開拓、航空 公司航線的增減、導遊人員的培訓以及政府駐外單位的配合措施，在在都 有賴於對未來需求的精確預測，過於粗略或不當的預測，不僅將造成大量 觀光資源的閒置與浪費，也將使得政府與觀光業者在這場日趨激烈的觀光 事業競爭中處於極不利的地位。本研究搜集並參考近十年來國內外學者在 觀光旅遊預測模式方面的研究，針對出國觀光旅客整體及各主要市場需求 ，尋找並建立適當之長短期預測模式。我們考慮下列六種模式：簡算法、 單變量時間序列模式、轉移函數模式、時間趨勢模式、指數平滑法以及計 量經濟模式，同時利用各類模式選取準則如AIC、SBC等來選取最佳模式， 或以平均絕對百分誤差(MA PE)、根均方百分誤差(RMSPE)、方向變化誤 差(Direction of Change Erro r)以及趨勢變化誤差(Trend Change Error)來評估各模式預測能力，從中選出最佳模式並進行預測整合分析。

本論文主要研究以類神經網路模式預測季節性時間序列之有效性。利用適 當地建構樣本訓練集,網路經訓練後可作為季節性時間序列之預測工具。 文中亦提出移動學習法以期提高預測之準確度。並以台灣地區每季進口商 品與勞務總值則作為實證之研究。此季節性時間序列因受離群值之影響而 增加其預測困難度。實證結果顯示類神經網路模式之預測表現較傳統之統 計方法優異,即使此序列受到離群值之干擾。 / We investigate the effectiveness of neural networks for predicting the future behavior of seasonal time series. Utilizing the training set constructed properly, we can train the network who can be used to predict the future of seasonal time series. A shifting-learning method is also employed in order to obtained a better forecasting performance. The quarterly imports of goods and services of Taiwan between the first quarter of 1968 and the fourth quarter of 1990 are studied in the research. The series are contaminated with outliers, which will increase the difficulty of forecasting. Empirical results exhibit that neural networks model free approach have better prediction performance than the classical Box-Jenkins approach, even the series are contaminated with outliers.

本研究主要探討幾個廣受市場投資人所使用的波動度預測模型，如歷史波動度法、指數加權移動平均法、GARCH、EGARCH以及隱含波度，另外再考慮近年才被學者提出的RLS模型與A-RLS模型，一同比較它們對於台灣市場波動度的預估能力，並擇一最優者，作為從事波動度交易的訊號依據。本文在進行波動度交易之實證，主要是利用選擇權與期貨組合、選擇權與delta期貨組合、跨式交易策略與勒式交易策略等四種廣為波動度交易者使用之波動度交易策略，進而比較它們在樣本外的交易績效。本波動度預測的實證發現，樣本內的預測能力，是以GARCH和RLS模型最佳，而樣本外的預估能力，則是GARCH表現最好。另外，波動度交易的驗證結果顯示，若持有至次一交易日即平倉，勒式交易策略於買進波動度時會有最高的績效，而當放空波動度時，則是跨式交易策略會有最佳的表現。

在解釋變數含測量誤差的簡單線性迴歸模式中，欲使第t+1期之產出Y達到某一目標值Y^*，則必需控制第t+1期投入變數Z，若參數α，β為以知時，可以將其設定為θ=(Y^*-α)/β。但當參數α，β為未知時，我們利用LSCE控制法則的設定方法，得到第t+1期設定的控制值Z_t+1，而且在機率為1下，Z_t+1 收斂至θ=(Y^*-α)/β。而貝氏最佳控制法則部份則是由第t+1期的預測期望損失，找出使其為最小的Z值即是所應設定的第t+1期控制值Z_t+1，並利用模擬結果來說明。

本文以事件研究法探討在公司治理程度不同的公司發布盈餘預測時， 不同類型的投資人在事件窗期中所反映的投資行為。首先以獨立董事與監 察人佔董監席次比例、董監事持股比例、大股東持股比例、經理人持股比 例、機構投資人持股比例、盈餘股份比以及董事長是否兼任總經理作為判 別公司治理程度的指標。再以公司發布盈餘預測的時點做區別，分別以當 年度首度預測是否由公司本身發布，與之後調高以及調低預測做為事件日。 實證結果顯示，當公司治理佳者發布盈餘預測時，外資會出現較多的買超 行為且獲得較高的異常報酬，散戶則持相反動作；而公司治理差者發布盈 餘預測時，散戶的買賣超變化量一般而言會較法人大，出現短期進出的情 形較多，且外資在其調高盈餘預測時，也不會馬上進行買進。整體而言， 外資在公司發布盈餘預測時所做出的交易行為，大致上與公司的治理程度 有正向關係，散戶則大多報持短線進出的態度。 / This study examines how different types of investors behave when entities with different corporate governances announce earnings forecasts. Using seven corporate governance indicators to categorize all of the samples and analyzing how the abnormal return, the results suggests foreign institutional investors are gaining more abnormal return by buying more stocks after earnings announcements of the entities with better corporate governance, and would not interact right after earnings announcements of the entities with worse corporate governance, when the individual investors are going the opposite way to the foreign institutional investors.

這篇論文研究跨國投資與監理寬容下保險公司之破產問題，同時論文的相關內容簡述於論文第一章中。第二章研究考慮匯率可預測下對跨國投資人資產配置的影響，結果顯示匯率可預測性能有效的提升投資人期末財富。第三章考慮監理寬容下保險公司的破產問題，在美國破產保護法第11章的架構下，保險人與被保險人之權利義務關係，可利用巴黎式選擇權描述，同時建構保證給付指標來衡量不同監理干預準則，數值結果顯示過於寬鬆的監理準則將導致被保險人的財務損失。第四章探討監理寬容下保險安定基金保險費率問題，依照美國破產保護法第11章的架構，安定基金保費可簡化成2個巴黎式選擇權，結果顯示，當前台灣保險單定基金費率有偏低的情形，建議主管機關訂定安定基金費率時需更加謹慎小心。 / This thesis focuses on the international portfolio selection and the bankruptcy cost of the insurance company under regulatory forbearance. The main theme of this thesis is outlined in chapter 1, which also serves as an introduction to the three papers (appearing here as Chapter 2, Chapter 3 and Chapter 4) collected in this thesis. In the theme of the international portfolio selection, Chapter 2 investigates the investment behaviors when learning effect is considered. According to the exchange rate predictability, the investor updates his information and adjusts his portfolio allocation. Finally, the numerical results show that the learning mechanism significantly improves the terminal wealth. In the theme of the regulatory forbearance, Chapter 3 provides an illustration of the impact on the ruin cost due to regulatory forbearance. The concept of the U.S. Chapter 11 bankruptcy code is employed to determine regulatory forbearance. Throughout the framework of Parisian option, a quantitative index of regulatory forbearance called Guarantee Benefit Index (GBI) is developed. The GBI is used to evaluate the different supervisory intervention criteria i.e., relative and absolute intervention criteria. Finally, numerical analysis is performed to illustrate the influence of different financial factors and the intervention criteria. Another important issue in bankruptcy problem is discussed in Chapter 4, i.e., the cost of insurance guaranty fund. It is important to determine the cost of bankruptcy when the insolvent insurance company is took over by the government. Under the U.S. Chapter 11 bankruptcy code, the cost of guaranty fund can be determined through Parisian options. Results show that the current premium rates of Taiwan insurance guarantee fund are far from risk sensitive. Hence the results suggest the government should more prudent to face the bankruptcy problem in insurance industry.

「預測市場」以未來事件為交易標的，透過網路平台彙整即時資訊，運用價格來判斷未來事件的發展，此研究方法同時具有「適當獎懲」與「連續修正」兩項特性。 本研究以文獻分析途徑探究預測市場在不同交易機制下的運作方式與市場價格決定過程，並據此分析不同交易機制之間的差異與影響預測市場運作的因素；除此之外，本研究另以個案研究途徑來探討「連續雙向拍賣」與「市場計分法則」兩個交易機制在價格準確度、市場流動性、價格炒作、參與誘因與莊家風險之間的差異。 本研究發現：若交易機制是連續雙向拍賣，則「0-100型」合約價格的預測準確度較高；若交易機制是市場計分法則，則「落點預測型」合約價格的預測準確度較高。連續雙向拍賣機制具有市場流動性不足的問題；市場計分法則機制面臨莊家風險的危機且不適用於市場競爭度高的環境；而上述兩種交易機制皆會出現價格炒作的現象。 / “Prediction market” is a research method based on immediate information collecting and organizing on the internet platform. With future events as the object of transaction, variations of the price of each transaction thus immediately provide the prediction of the development of future events. Therefore, this method has two properties including “appropriate incentives” and “continuous correction”. In this study, document analysis is first conducted to review the operation modes of different trading mechanisms for prediction markets and the process of price making. Accordingly, differences between trading mechanisms and the factors that affect the operation of prediction market will also be analyzed. Furthermore, comparisons of the price accuracy, market liquidity, price speculation, incentives and maker risks between "continuous double auction" and "market scoring rule" are discussed in case study. The findings of this study: if the trading mechanism is “continuous double auction”, the price accuracy of “winner-take-all” contract is higher; if the trading mechanism is “market scoring rules”, the price accuracy of “index” contract is higher. There exists insufficient market liquidity in “continuous double auction;” while in “market scoring rule,” there exists maker risks and it is hard to be applied in highly competitive market. The phenomenon of price speculation exits in both trading mechanisms.

近年來社群平台(Social Media)的興起，提供了人與人之間簡便且快速互相交換各式各樣內容的機會。社群多媒體(Social Multimedia)指的就是使用者在社群平台上所互相交換的多媒體內容，相較於單純的多媒體內容而言，社群多媒體多了寶貴的大量社群平台使用者之間分享互動的記錄，以及社群平台使用者在社群網絡(Social Network)中的各項資訊。如此一來為多媒體內容提供了更多面向的資料，讓社群多媒體比起單純的多媒體內容有更多的應用的可能。 微網誌(Microblog)是個可以讓使用者自由的即時分享文字訊息的平台，有著許多使用者的當下的心情、眼前所看到聽到的事或與朋友對話等。而微網誌平台相較於其它單純用來分享多媒體內容的社群平台(例如YouTube或Flickr)而言，在微網誌平台上的多媒體內容有明顯的分享傳遞現象。而本研究的目標，就是要利用些多媒體內容在微網誌平台上的分享傳遞的特性與資料，針對群多媒體內容進行熱門預測。 隨著時間的前進，若以單一同樣的規則來進行熱門預測，將可能造成預測準確率的下降；再者，即使是在同樣的時間點，不同的多媒體內容會有各自隨著時間在熱門上的變化趨勢，還是會有需要不同的規則來進行熱門預測的可能性，也就是所謂的局部概念飄移現象。在此我們將熱門預測問題轉為資料探勘(Data Mining)中的分類(Classification)問題，並同時將局部概念飄移現象納入考慮，提出一個針對微網誌平台上多媒體內容的熱門預測方法。實驗結果顯示，有考慮局部概念飄移的熱門預測方法，在準確率的表現上明顯的優於GCD方法(平均有4%的提升)與Baseline方法(平均有10%的提升)，代表我們的熱門預測方法更適合微網誌平台上的多媒體內容，也代表的確有概念飄移與局部概念飄移的現象存在。 / In recent years, the rise of social media offers an easy and fast way for information exchange. Social multimedia refers to the multimedia content that users share on the social media. Different from traditional multimedia, social multimedia contains both the multimedia and user behavior information on social media. Microblog is one type of social media. Compared to other social media such as YouTube and Flickr, microblogs provide a more friendly environment for users to propagate social multimedia. The goal of this thesis is to make use of the characteristics and information of propagation on microblogs for popularity prediction of social multimedia. The popularity prediction method based on concept drift mining is proposed. In particular, the local concept drift mechanism is employed to capture the local characteristics of social multimedia. By taking the local concept drift into consideration, the task of popularity prediction is transformed into the ensemble classification problem. Experiments on social multimedia collected from plurk show that the proposed approach performs well.