國際研究中雖有針對國家級的銀行脆弱性作分析，卻並未定義或預測台灣系統性危機，本研究在這樣的背景下，決定建構台灣本土的銀行業預警系統，建立銀行危機的領先指標，希望不只順應國際潮流，更能發展適合台灣特殊性的模型。本研究利用貝氏網路模型的特殊性: (1)事後值(2)機率特性，以個體化資料著手，建構一總體性模型。故研究者能確切了解個別銀行財務狀況，對個別銀行發出預警。事後值的特性使研究者能同時考慮多項財務比率。另外，利用機率特性，可幫助研究者了解危機的程度，且能做總體的延伸運用。 本研究發展出兩種方法建構總體模型。第一種為百分比法，以危機銀行佔總銀行個數的比率為基礎；第二種為加權平均法，讓機率值高者有較大權數，機率小者有較小權數去建立一加權平均機率值。 將本研究的推論結果和「台灣金融服務業聯合總會委託計畫-台灣金融危機領先指標之研究」比較，顯示本模型的兩種方法皆與危機之發生有相同趨勢，而考慮危機訊號的設定後，方法二加權平均法顯然具備較佳的預測結果。此外相較總體面衝擊產生的危機，本模型在預測能力上，對來自銀行個體面造成的危機預測明顯較優異。 / International organizations defined and predicted country bank crises events without Taiwan, but they happened in Taiwan in the past twenty years. We construct the early warning system for banking crises in Taiwan and develop the specific model suited to our country. Using Bayesian Model’s specialities: (1) posterior value; (2) probability, we build a systematic model based on microeconomic data. So researcher can understand all financial conditions and predict the financial distresses of individual banks. The concept of posteriority lets researchers can consider a lot of financial ratio at the same time. The characteristic of probability makes researcher to extend the model to macroeconomic. We develop two methods to build systematic model. One is Percentage method which is based on the percentage of financial distress banks to all banks. The other one is weighted average method which used large weight in financial distress bank and small weight in financial sound banks. Comparing our results with the report that Taiwan Financial Services Roundtable issued in 2009, our methods have distress trends which link with crisis directly. But weighted average method has a better predict power than percentage method after considering the signals of distress we specify. Besides, our model has a stronger predictive power in crises from individual effect than crises from macroeconomic shocks.

為有效減少因不可預期的極端氣候災害所產生的傷亡，可透過提升災害預警機制與災害認知使民眾具備更佳的災害應變決策能力。本研究選擇以台灣地區仍不常見的堰塞湖災害為研究對象以呼應極端氣候不可預期的特性，首先整理出居民在進行災害應變決策時的思考模式與程序，並釐清影響最後決策的各項因素，作為本研究主要研究架構的初擬參考；在分析工具方面則選用結構方程模式(Structural Equation Modeling, SEM)，以同時解決將心理層面認知量化與各變項間直線迴歸關係的問題。 為將以上構想付諸實行，本研究先經由文獻回顧提出居民的災害應變決策概念架構，以此概念架構為基礎進行問卷設計，並在台東縣嘉蘭村與高雄市瑪雅里進行問卷調查作業，取得資料後再透過結構方程模式建立適合台灣地區民眾的堰塞湖災害決策模型；最後則針對本研究所建立的模型與分析，對現況風險溝通與預警機制提出相關政策建議。 在試圖達成前述目的的研究過程中，本研究發現台灣民眾在面對不熟悉的堰塞湖災害時，外在的預警訊息、過去的受災經驗，以及家戶狀況等三項因素對決策影響最大；另外雖然本研究所提出的決策架構在台東與高雄兩處受測範圍內均可適用，但仍會因聚落的受災經驗、居民屬性，以及交通區位等特性上的不同而在變項重要性上有所差異。最後，本研究提出相關風險溝通的策略建議，作為未來在面臨堰塞湖災害或其他極端氣候時的政策參考。 / In order to reduce the unpredictable and extreme weather disasters’ casualties effectively, we can improve disaster warning mechanisms and disaster awareness so that people have decision-making capacity for better disaster response. This study selects the Landslide dam’s disaster, which is not common in Taiwan, as the research object. First sorted out the residents’ disaster response policy and procedures of thinking, and clarified the factors affect the final decision as the beginning research framework of this study. Analysis tools are used in structural equation model (SEM), to address both the psychological aspects of cognitive variables to quantify and the question of the relationship between the linear regressions. On the research process, first put forward the residents’ conceptual framework for disaster response decision-making through the literature review, and using this conceptual framework as the basis for questionnaire design. Taitung County and Kaohsiung City are the questionnaire surveys in this study. After the data obtained, we established a suitable decision model for Taiwan’s Landslide dam disaster through the SEM tool. Finally, we can make recommendations on risk communication policy and early warning mechanism through this model. In the research results, this study found that the external warning message, past disaster experience, and household conditions are three of the most important decision factors when Taiwan public is facing the Landslide dam disaster. In addition, although this framework can be applied in Taitung and Kaohsiung areas, but there will still be differences because of the differences in some characteristics such as the affected experience, residents’ properties, and location of traffic. Last, this study proposes some strategies of risk communication as the policy reference when facing the Landslide dam’s disaster or other extreme weather disasters in the future.

有鑑於證券商走向大型化、業務多樣且複雜化，同時面臨激烈之競爭環境，台灣證券交易所及證券櫃檯買賣中心乃開始研議建置綜合評等機制以利未來對證券商之監管，現行對證券商之評等主要有資本適足率、證券商整體經營風險預警制度、風險管理評鑑、及例行查核對內部稽核作業評分等四項。主管機關目前係依個別項目作為證券商業務准駁和經營業務限額之控管(例如：證券商買賣倍數採用資本適足率和整體經營風險之評等、辦理有價證券借貸業務和證券業務借貸款項之核准採用資本適足率和例行查核對內部稽核作業評分、財富管理業務之核准採用資本適足率、資本適足新制度下選擇權部位市場風險約當金額採行敏感性分析（delta-plus）法之核准採用風險管理評鑑…等等)，然而，因不同之業務採用不同之評等項目與標準，項目過於繁雜且不一致。因此，本文之研究目的為建置適合我國採用之證券商整體綜合評等模式。 本文考量我國現行對證券商之差異化管理，並參考國際與我國金融監理機構之差異化管理之文獻後，提出一個整體之綜合評等機制，建議可比照美國堪模斯(CAMELS)綜合評等模式機制，將我國現行對證券商評等之「資本適足率」、「證券商整體經營風險預警制度」、「風險管理評鑑」、及「例行查核對內部稽核作業評分」等項目，同時納入考量以建立整體性之綜合評等機制，以進行分級管理，並作為對新業務申請、新營業據點申請、檢查頻率高低、證券商負責人或從業人員處分等之參考。

本財務預警模型研究延續Shumway(2001)年所提出的離散型風險模型(Discrete-time Hazard Model)架構，即Shumway 所稱之多期邏輯斯迴歸模型(Multiperiod logistic regression model) ，來建立銀行財務預警模型。不同於Shumway所提出的Log 基期風險式，研究者根據實際財務危機發生機率圖提出Quadratic 基期風險式。由於離散型風險模型考量與時間相依共變量(Time-dependent covariate)，該模型可以納入隨時間變動的的市場與總體變數，這是單期模型無法達到的。實證結果顯示，不論是否有加入總體與市場變數，Quadratic 基期風險式離散型模型在樣本內檢測表現都比單期模型與Log 基期風險式離散型模型好，研究亦顯示樣本外的預測Quadratic基期風險式在大多數情況都優於Log 基期風險式與單期模型 / This paper continues Shumway(2001) studies on discrete time hazard model, the so called multi-period logistic regression model, to develop a bank failure early warning model . Different from log baseline hazard form proposed by Shumway, author present quadratic baseline hazard form based on the pattern of real default rate. By incorporating time-varying covariates, our model enables us to utilize macroeconomic and market variables, which cannot be incorporated into in a one-period model. We find that our model significantly outperforms the single period logit model and Log baseline hazard model with and without the macroeconomic and market variables at in-sample estimation. The improvement in accuracy comes both from the time-series bank-specific variables and from the time-series macroeconomic variables. Our research also shows that quadratic baseline hazard model outperforms Log baseline hazard model and single period logit model in out-of-sample prediction.

根據世界銀行的資料顯示，佔全球面積約19%的2500萬平方公里之地球表面，及佔全球一半以上人口的34億人是相對的暴露在一個以上天然災害之威脅下。隨著全球化的來臨，我們居住在一個風險共享的社會中，而在全球環境安全被視為全球公共財的同時，如何在集體行動的邏輯下進行全球危機管理，已成為全球行動者的主要課題。例如如何透過國際合作來對抗SARS，禽流感等危機，皆是當前全球行動者關注的議題。值得注意的是，儘管近二十年來國際社會對於減災所做的承諾與投入的資源日益增加，災害所造成的經濟損失及受到災害影響之人口卻也逐漸上升。面對這些現象，本研究最主要想要探究的研究問題即在於什麼樣的因素影響著全球減災合作。 本研究主要的研究問題，係探求在全球行動者為何要參與減災合作，而此全球減災合作又如何運作的呢？全球減災合作、理性選擇與全球社會資本的分析架構將被運用。從理論上粹取的因素，例如風險意識、能力素養、偏好、制度限制、資訊、可信的承諾與信任等，被用來分析行動者如何決定參與合作，以及此合作如何運作。鑑於2004年印度洋海嘯所造成的重大傷亡以及後續國際社會對救災及減災的承諾，本研究將以印度洋海嘯預警系統的個案為例，並透過在4個國家共計22人次對參與此系統的國際行動者之訪談資料，以及對參與印度洋海嘯預警系統之人員發放共計591份問卷進行調查及分析，回收問卷目前共計61份，然進行論文分析時為59份。換言之，實際上的回收率為10.66%，而本研究用以分析之問卷回收率為10.32%。本研究最主要的發現為風險意識及能力素養的提升，結合理性選擇與社會資本的不同因素作用下，將對全球減災合作的結果有正面的影響。最後，本研究也對未來國際減災合作提出相關之建議。 / Writing on the issue of global environmental security, the World Bank has noted that approximately “25 million square kilometers (about 19 percent of the Earth’s land area) and 3.4 billion people (more than half of the world’s population) are relatively highly exposed to at least one hazard.” With the coming of the globalization era, we .also live in a shared risk society. Since global environmental security is seen as a global public good, how to act for global crisis management under the logic of collective action has become a primary subject for global actors. Coping with the crises of SARS or Bird Flu through international cooperation has become a significant issue for these global actors. One of the main dilemmas of international cooperation for disaster reduction is the reconciliation of different individual actions. Interestingly, in spite of two decades efforts of international cooperation, the amount of damage caused by natural disasters and the total number if people affected have gradually increased since the 1960s. This research focuses on two questions in the present research: why do global actors cooperate in disaster reduction, and how does this cooperation operate? The frameworks of international cooperation in disaster reduction, rational choice and global social capital are employed here, to explore the issue of international cooperation. Several factors, such as awareness of risk, capacity, preferences, institutional constraints, information, credible commitment, and trust, are used to examine how an actor engages in decision-making and how cooperation occurs. Because of the tremendous damage that resulted from the Indian Ocean tsunami of 2004 and the engagement of the global society in disaster recovery and reduction, the above issues will be explored through a case study of the development of the Indian Ocean Tsunami Warning System (IOTWS). Twenty-two interviews were conducted in four countries and these constitute the qualitative data for this analysis. 591 questionnaires also have been sent to the participants in the IOTWS to collect the quantitative data. I analyzed the quantitative data from 59 returned questionnaires (10.32% returning rate) and the qualitative data from 22 interviewees in four countries. These analyses resulted in several suggestions to facilitate international cooperation for disaster reduction.

金融海嘯提供我們一個深刻的教訓，因為危機前信貸過度增長伴隨著大量的系統風險，最後導致景氣反轉時銀行業龐大損失。而這些損失將動搖整個金融體系，並引發了一連串的惡性循環(Basel Committee on Banking Supervision , BCBS ,2010a, 2010b)；若依循過往個別審慎監理((Microprudential regulation)原則，將無法察覺背後隱藏的系統風險。因此目前趨勢是將以個別(Micro)與總體審慎監理原則(macro)並重，針對能夠影響整體市場金融穩定風險來源而詳加監管，同時透過規範與監理措施適度的降低系統風險，最終達到金融穩定的目的。IMF、BIS以及FSB(2009)針對G20制定的金融機構、市場與工具的指導文件(Guidance to Assess the Systemic Importance of Financial Institutions, Markets and Instruments)中，認為有效控制系統風險是現階段政策監理最重要的主軸之一。所謂系統風險是指能影響金融機構所持有的部位以及對於實體經濟存在嚴重負面影響的風險來源；此總體風險將存在負外部性而非個別審慎監理的風險因子。 因此本文由兩篇宏觀審慎監管框架文章所構成的研究，分別針對市場流動性風險和信用風險的因子。透過非流動性賣權與逆景氣資本緩衝（CCB）買權來分析和評價兩種新的監管框架。第一篇論文的主要概念是討論市場流動性風險因子，雖然當前銀行監管的重點是資金流動性風險，如新巴塞爾協議三 (Basel III)的流動性風險覆蓋率（LCR）和淨穩定資金比率（NSFR），但金融機構實際上也同時面臨資金和市場流動性之間的高度順週期效應，導致流動性螺旋，並威脅到金融穩定。因此，本文提出一個市場流動性，系統性風險和宏觀審慎監理分析框架來填補這一空白。 與Drehmann和Juselius（2013B）的實證研究結果比較，我們發現利用6個月歷史波動度建構的非流動性選擇權是最有效的提前預警指標（EWIS），且符合穩定政策結構和最小監管成本。此外在三個子樣本和嚴重危機時期亦能同樣保持預警的穩健性。因此如果金融機構能透過預警減少金融機構投資種類、行業、交易對手與大額暴險的集中度時，將可以由危機發生後被動式轉變成危機發生前主動式的風險管理，將符合總體審慎監理定義：能影響所有而非單一的金融機構，以及有效控制破壞總體市場產生的系統風險。 在第二篇文章中，我們專注於信用風險監管框架的避險，即Basel III的逆景氣資本緩衝(CCB)。這個新穎的監理視角將鼓勵銀行在危機前的信貸繁榮時期增加資本緩衝，而非在危機後接受援助或者增加昂貴的資本。據美國聯邦存款保險公司（FDIC）統計，2014年第1季全美的存款機構風險加權資產為10.27兆美金；如果最高的逆景氣資本緩衝被應用到這些銀行，將有2570億美金的資本不得不額外注資。因此本文設計了一個新的買權來符合CCB的監管框架，建立提前資本防禦措施來減輕系統性風險和整體銀行業不穩定。首先發現這款買權將能在順境時注入資本，即更低的潛在違約風險與信貸寬鬆時期，進而抵禦未來發生的金融危機。我們的建議也符合Basel III的目標，在危機前2至5年協助銀行取得資本保護。最重要的是，CCB買權可以透過提前取得資本形成一個“減震器”，舒緩隨後而來經濟衰退的壓力達到降低銀行資本順週期性目標；此外還提供了一個對於銀行過度冒險行為的抗衡力量，成為一個“自動穩定器”來達到宏觀審慎監理目標。 / Financial tsunami offered a profound lesson as the pre-crisis excessive credit growth was accompanied by huge systemic risks that ultimately led to the reversal of economy and huge losses of the banking sector. Such losses will shake the entire financial system and trigger a series of vicious cycles (Basel Committee on Banking Supervision, BCBS, 2010a , 2010b ); the hidden systemic risk may not be observed if we follow the previous principles of micro prudential regulation. The guidance formulated by G20 to assess the systemic importance of financial institutions, markets and instruments (IMF, BIS, and FSB, 2009) analyzes that the main issue of prior micro prudential regulation is that every financial institution’s incentive is to manage its own return-risk tradeoff but not necessarily manage the stability for the financial system as a whole. Consequently, the macroprudential regulation focusing on shocks originating outside the financial system can control the negative externalities of systemic risk rather than micro prudential regulation. This dissertation consists of two essays on the macro prudential framework of market liquidity risk and credit risk factor. We introduce, analyze, and value two new regulation frameworks via an illiquidity put option and a CCB call option respectively. The main concept of first essay is to discuss the macro prudential framework of market liquidity risk factor. Although the current banking regulation focuses on systemic funding liquidity risk such as Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) of Basel III, financial institutions would actually have highly procyclical effects between funding and market liquidity at the same time, leading to liquidity spirals and threatening to financial stability. We therefore propose a market liquidity, systemic risk and macroregulation analysis framework in Taiwan's capital market to fill this gap. Comparison with the Drehmann and Juselius' empirical study (2013b), we find that illiquidity options by using 6-month historical volatility and forecasting short-term stock declines are effective early warning indicators (EWIs) having most stable policy structures and minimal regulation costs. Applying AUC macroregulation criteria, we show this illiquidity measure is also maintained fairly robustness in different intervals, e.g. during three sub-samples and serious crisis periods. If financial institutions can diversify the concentration of portfolios varieties, industries, and counterparty before crises by using EWIs, the passive risk taking can be converted into the active risk management. It is necessary to prepare the market liquidity and macroregulation framework in advance. In the second essay, we focus the hedging product for credit risk factors, i.e. countercyclical capital buffer (CCB). This purpose of countercyclical capital buffer standards is to encourage banks to increase capital buffers in credit good times that can be used in the future stress. According to Federal Deposit Insurance Corporation (FDIC), the risk-weighted assets of U.S. depository institutions were $10.27trillion dollars in 2014:Q1. If the maximum CCB is applied to these banks, an additional US$257 billion of equity capital will have to be raised. Hence, we design a new option to establish the capital defenses meeting CCB framework and then mitigating systemic risk and banking instability in advance. We show this product injects capital in good times i.e., lower credit risk and more credit expansion, to weather the future financial crisis. Our proposal also complies with the goal of Basel III to obtain capital in 2 to 5 years prior to crises. Most importantly, the CCB option can provide protection with additional capital to act as a "shock absorber" reducing a procyclicality problem in the subsequent downturn. Besides, this type of option also offers a countervailing force to excessive risk-taking behaviors to act as an "automatic stabilizer" for reaching macroprudential goals.

本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。 本研究之結論為： 一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。 二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。 三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio. The conclusion of this research are: Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items. Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items. Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.