國內生產毛額作為總和國內經濟狀況的綜合性指標, 一直是政府機關與民間機構在進行決策時的重要參考之一。 然而, 也因為需要整合較多的統計資料做計算, 國內生產毛額因此僅有季的低頻資料。 為了能夠精準地預測此類低頻資料, 多數學者遂以數學模型將高頻與低頻資料做連結, 期能透過模型找到高頻資料所隱含的資訊來預測低頻資料, 即期預測 (Nowcasting) 即是此類型預測的概稱。 透過即期預測, 我們可以快速掌握當下的經濟狀況, 以做出更合適的決策。 據此, 本文將依Banbura, Giannone and Reichlin (2010),以狀態空間模型 (State Space Model) 搭配卡爾曼濾波器 (Kalman Filter) 來 實現對國民生產毛額的即期預測, 並藉此模型對我國經濟體進行相關分析。

1998年中國大陸改革開放不動產市場後，由於政府大力地推動城鎮化與不動產市場改革以及中國大陸人均GDP的成長快速等原因，使房價快速上漲。2008年金融危機後至2012年時，中國大陸房價上漲約63.31%，但居民收入僅增加55.66%，顯示房價上漲速度超越所得上漲速度，因此，本研究擬由資產現值模型建立房價基要價值，並由狀態空間模型推估泡沫價格，探討北京市、天津市、上海市與重慶市不動產市場是否存在泡沫化的現象。經由1998年至2012年的家戶所得推估泡沫價格後，再以向量誤差修正模型與Granger因果關係檢定檢驗泡沫價格與貨幣供給額、預期物價指數、購屋貸款利率、住房開發投資額與前期房價成長率間的關係。 實證結果指出，北京泡沫化幅度變動劇烈，2012年第2季泡沫化約57%，由於中國大陸政府對北京執行政策較為嚴格，因而使北京市的房價受到政府政策的影響而產生較劇烈地波動。天津的泡沫價格則是由2004年開始轉為正值，並於2006年第2季達到第一波高峰。上海房價呈現穩定上升，其泡沫化程度約維持在45%上下，其泡沫化高點出現在2010年，泡沫價格占房屋價格約46%。重慶房價於2004年開始大幅上升，並於2011年出現泡沫高峰，比重約為40%。另外，預期通貨膨脹率與住房開發投資額為Granger領先於北京、天津與重慶的泡沫價格，表示政府能藉由控制北京、天津與重慶的預期通貨膨脹與不動產開發投資市場，來降低不動產的泡沫價格。而上海的購屋貸款利率、前期房價成長率與泡沫價格為雙向因果關係，貨幣供給則為Granger領先於上海泡沫價格，表示政府若能藉由控制上海的貨幣供給與購屋貸款利率，降低其泡沫價格。

零售業是美國最大的產業之一，近年來科技進步以及網路購物擁有價格優勢、交易方便等優點，未來電子商務將成為主流的銷售形式之一，一般實體零售業者如何因應這股潮流是一大課題。 　　與本研究有關之美國服飾零售業，實體店家還是占市場的多數，因此，為了提升服飾零售實體店家的競爭優勢，我們預測來店人數，一方面調整人力資源的分配與進貨量，提供顧客優良的服務品質，另一方面視情況提出促銷方案吸引顧客上門，進而提升營運效率。 　　每年從感恩節到聖誕節這一個月的時間，是關乎全美零售業生存與否的重要時刻，這段時間的銷售額約占全年銷售總額的1/5，也就表示來店人數在這段期間會維持在一定的數值以上甚至達到全年巔峰，而如何不受影響達到精準預測？本研究欲找出指數平滑法中適合的模型精準預測來店人數的資料。 　　本研究旨在探討指數平滑法與延伸之狀態空間模型，指數平滑法屬於時間序列(Time series)的預測方法，是應用相當廣的一種預測方法，一般由趨勢(Trend)以及季節性(Seasonality)組合而成，而將指數平滑模型加入誤差項以後的狀態空間模型，過去一直沒有一個隨機模型做為架構納入概似估計與預測區間等，近幾年才發展出模型之最佳化準則來估計參數，而本研究想探討哪一個狀態空間模型適用於預測來店人數資料以及狀態空間模型之最佳化準則是否能使預測結果更準確。 　　本研究之資料為美國時尚精品服飾店2007年營業時間內每小時來店人數，而實證分析後發現Holt-Winters季節性加法模型ETS(A,A,A)蠻適合用來預測來店人數，此外ETS(A,A,A)模型之最佳化準則以AMSE準則與MLE準則表現最佳， MAE準則表現最差。

在台灣，遠期外匯可分為有本金遠期外匯(DF)及無本金遠期外匯(NDF)，其中無本金遠期外匯為銀行與客戶訂定之無標準化規格契約， 其特色是在契約到期時，交易雙方僅就約定之匯率差額進行交割，不須交割本金。此特色也使得避險或是投機時較為節省資金成本， 故NDF在台灣遠期外匯市場的交易量有逐漸增加的趨勢。 然而在理性預期下，不論是DF或是NDF都應該是即期匯率的最佳預測值，即所謂的市場效率性。傳統統計方法通常用線性迴歸來檢定市場效率性， 但卻常推估出互相衝突的結論。本文利用Kalman approach推估遠期外匯之貼水，希望藉此觀察出不同時間點，台灣遠期外匯市場的效率性。 研究結果發現台灣遠期外匯之貼水在金融風暴之後呈現穩定，表示此時間內台灣外匯市場具有效率性。 另外，在金融風暴之後NDF貼水之波動較DF而且為大，表示程度上NDF較不具效率性，可能跟NDF之投機性交易較多有關係。 雖然如此，NDF市場之投機交易，並沒有使NDF之貼水波動達到無效率的地步，故建議央行可逐步放寬對NDF交易之限制， 以促進市場交易之健全。 / The forward exchange are divided into deliverable forward(DF) and non-deliverable forward(NDF) exchange in Taiwan . NDFs are foreign exchange derivative products traded over the counter. The parties of the NDF contract settle the transaction, not by delivering the underlying pair of currencies, but by making a net payment in a convertible currency proportional to the difference between the agreed forward exchange rate and the subsequently realised spot fixing. Under the rational expectation of foreign traders, not only DF exchange rate but also NDF will be the best predictor of the spot exchange. Tradional statistics methods use linear regressions to test whether the markets are efficiency or not. However, this study consider a Kalman approach to estimate the model and predict the spot exchange rate. The results can be found by observing the estimated premia: first, the premia show a certain degree of persistence after the Asian crisis. Second, the premium of NDF rate is more fluctuated than DF rates after the Asian crisis. It may present that the Non-deliverable forward exchange market in Taiwan has many speculative transactions. However, considering the process what we analyze the difference between the future spot rates and forward rates, it seems that the forward exchange markets in Taiwan have efficiency because of their persistence over time. Since the speculative transactions have no enough power to make the NDF markets inefficient, the Central Bank of Taiwan may suggest cancel the restrictions of NDF transactions.

過去雖有文獻探討國內房地產市場泡沫化問題，卻僅從租金收益的單一角度衡量房價基值，對於自有住宅比例較高的台灣而言，家戶所得不僅代表購屋者的負擔能力，更是構成房價基值的重要因素。有鑑於此，本研究分別從租金收益及家戶所得兩者不同角度下，透過資產市場現值模型，分別建立房價基值模型分析泡沫化現象。此外，過去文獻僅從檢定價格波動穩定性與否或將殘差項視為泡沫來研究泡沫化問題，然泡沫為不可觀察之變數，故本文使用具有可估計不可觀察變數特質的狀態空間模型（STATE-SPACE MODEL），推估泡沫價格，分析在不同時期下泡沫的規模大小。 在實證方面，本研究使用台北市1973Q2至2008Q1共140筆住宅價格資料，發現由租金與所得所計算之房價泡沫規模略為一致。在1988~1990年房市泡沫化時期，所得推估之泡沫規模達到高峰，泡沫價格占市價約47%；而由租金面亦計算出泡沫價格占市價約54%的高比例。而在2008年房價持續上漲的情況下，兩者泡沫價格亦呈現相同上升之走勢，泡沫價格近市價38%，租金推估泡沫價格占市價27%；此結果表示出目前房市有泡沫化之跡象，現階段欲購屋自住者不宜進入市場，宜審慎等待時機。而本文認為房價所得比或是房價租金比皆是作為衡量台北市房地產市場泡沫化現象之重要指標，另外就總體因素分析而言，房價上漲率、貨幣供給額、貸款利率與大盤股價指數皆為影響泡沫之重要因素，且經由實證發現所得所推估之泡沫價格較具有市場代表性。 / The past literatures about Taipei housing price bubble has only been measured the fundamental price by rent. However, the housing owner ratio is so high in Taiwan that housing income is not only regarded as affordability but also an important fundamental factor of housing price. According to the above, we focus on different fundamental models that define market fundamental price to analyses the bubble price from expected present value of both rent and permanent housing income. On the other hand, different from lots of literature testing the housing price volatility or residual to measure bubble prices, because housing bubble is an unobservable variable, we apply State-Space Model which is good for testing an invisible factor to estimate bubble in the housing markets of Taipei. This paper tries to test whether there was a housing price bubble using Taipei housing price index ranged from 1973Q1 to 2008Q1. The findings indicate that there appeared bubble ratio from 1988 to 1990, 47% of the housing price based on housing income and 54 % of the housing price based on rent. In 2008 when housing price continually keeps rising, bubble price ratios are close to 38% and 27% respectively. Those results show that Taipei seems to have sign of a bubble in this moment and housing buyers should concern it with more caution. Secondly, both price-income ratio and price-rent ratio are good indicators to measure housing bubble prices. Beside, we find macro economic factors change, such as the growth rate of housing price, M2, mortgage rate, and stock price index, are important to influence the size of housing bubble. Thirdly, bubble price estimated by housing income has a better performance than rent.

本研究透過聯合系統與獨特風險綜合評估違約的強度，假設市場上經濟變數或資訊影響系統之違約強度，然若直接考慮所有經濟變數到模型中將可能會有共線性或維度過高之疑慮，因此透過狀態空間模型來設定狀態變數以及經濟變數之關係並將萃取三大狀態變數分別用以描述市場實質活動面、通貨膨脹以及信用環境。另外，將透過結構式模型來計算獨特性風險大小，當個別潛在的變數低於一定數值將導致個別的違約事件發生。而因布朗運動可能無法描述或校準市場上違約之鋒態以及偏態，將進一步考慮Variance Gamma過程用以更準確描述真實違約狀況。最後透過結合以上兩個風險綜合評估下，考慮一個聯合違約模型來評價信用違約交換之信用價差。 / Systematic and idiosyncratic risks are supposed to jointly trigger the default events. This paper identifies three fundamental risks to capture the systematic movement: real activity, inflation, and credit environment. Since most macroeconomic variables fluctuate together, the state-space model is imposed to extract the three variables from macroeconomic data series. In the idiosyncratic part, the structural model is applied. That is, idiosyncratic default is triggered by the crossing of a barrier. For improvement of the underlying lognormal distribution, we assume the process for the potential variable of the firm follows a Variance Gamma process, sufficient dimensions of which can fit the skewed and leptokurtic distributions. Under the specific setting of combinations of the two risks (the so-called joint default model), we price credit default swaps.

違約事件受到系統性風險與獨特性風險的綜合影響。本研究建構一狀態相依公司信用模型，該模型能反映出系統環境對市場造成的影響與個別公司獨特因子帶來的個別衝擊。 本模型透過從總體環境中萃取出的狀態變數來捕捉系統性變化，另外透過Variance Gamma過程來描繪個別公司的獨特因子帶來的影響。Variance Gamma過程可藉由調整分配的鋒態及偏態來調整布朗運動無法反映出的分配，以更貼近真實的市場訊息。 與縮減試模型相較之下，本模型無需參考信評機構的信用評等資訊，僅依賴市場上公開且透明的資訊，並且與結構式模型相同的是其富有經濟意涵。我們可以透過本模型來同時生成公司流動性危機發生機率與預期流動性危機造成的損失，進而利用本模型評價出個別公司信用違約交換的價格。 關鍵字：信用違約交換；系統風險；獨特性風險；狀態空間模型；Variance Gamma 過程 / Systematic and idiosyncratic risks are thought to affect the default events. This study develops a state-dependent corporate credit model that reflects both systematic movement and idiosyncratic shocks. To capture the systematic movement, the model extracts state factors from macroeconomics data. For the idiosyncratic part, the model applied Variance Gamma Process in depicting the potential variable of the firm by altering the distribution’s skewness and kurtosis. The model contains abundant economic significance as structural-form model does. Comparing to the reduced-form model, it does not rely on the information provided by rating agency but use information that is transparent and public. One can generate a firm’s probabilities of liquidity crisis and expected liquidity shortfalls endogenously and concurrently by employing the model. Credit derivative such as Single-name CDS can be priced under the model.

本文旨在探討我國上市公司除了盈餘外，資本支出是否具有增額資訊內涵。研究內容分為兩個部份，第一部份在使用狀態空間樣型探討資本支出是否有助於預測未來盈餘；第二部份根據本文推導之模式探討資本支出資訊與股標報酬之關聯。〔分為年度研究與長期研究（兩年及三年）兩個部份〕。研究結果發現： 1.除了盈餘資訊外，資本支出無法幫助吾人預測未來盈餘。 2.無論係長期或年度研究，均呈現股價領先財務報表期間現象。在長期研究中，本文發現大公司之股價有post announcement drift現象。 3.盈餘、資本支出資訊（或財務報表資訊）對股價之解釋能力視股市係屬多空頭市場而定。多頭市場解釋能力較高。 4.長期研究財務報表對股價解釋能力高於年度研究。 5.盈餘水準、資本支出水準均具增額資訊內涵，不論長期或年度研究。 6.非預期土地投資與非預期廠房設備投資對股價均具解釋能力。 7.未預期資本支出反應係數之影響因素： (1)就成長機會而言： 股票市價╱權益比愈大之公司，營收成長率愈高之公司未預期資本支出反應係數愈大。 (2)就系統風險而言： β值愈大之公司，未預期資本支出反應係數愈大。 (3)就資本支出報酬率而言： 盈餘水準、盈餘持續度愈大之公司，未預期資本支出反應係數愈大；而自有資金比率愈低之公司，未預期資本支出反應係數愈大；小公司之資本支出反應係數較大；研發水準愈高之公司，未預期資本支出反應係數愈大。 (4)就資本支出受益年限而言： 本文以產業進入障礙為資本支出受益年限之替代變數。研究結果發現各行業之資本支出反應係數與產業進入障礙正相關。 (5)資本支出型態 規模成長型公司之資本支出反應係數大於汰舊換新型公司。 / This study aims to examine the incremental information content of capital expenditures of Taiwan listed companies. Taiwan listed companies generally have intensive capital expenditure rather then research and development costs in order to sustain the growth of their performance. Thus, this study suspects that the level of capital expenditures could help predict future earnings upon which capital expenditure could incrementally explain the earnings/return relationship. Empirically, this study first investigates the relationship between current capital expenditure and future earnings. Second, in order to select the optimal earnings/return windows, this study simulates the returns window for large and small firms over various long windows. Third, this study extends Collins and Kothari (1989) and Feltham and Ohlson (1995) to investigate whether the capital expenditure would contain an incremental information content in terms of earnings/return relationship. The findings of this study can be summarized as follows. 1.Besides earnings itself, the capital expenditure cannot well predict future earnings. 2.No matter what is temporal or cross sectional study, price leads the realization of earnings. In addition, the large firm sample group demonstrates the phenomenon of post-earnings drift. 3.The capital expenditure has more explanatory power to earnings/return relationship in the bull market than in the bear market. 4.Earnings and capital expenditure level have incremental information contents in terms of earnings/return relationship. 5.Both unexpected property and unexpected plant investments have explanatory power to the stock price. 6.The determinants of capital expenditure response coefficient, including growth opportunity, systematic risk, returns on capital expenditure, beneficial period of capital expenditure, and types of capital expenditure can increase the explanatory power of earnings/return relationship.