大都市憑著其高度的聚集經濟、高生產力，而不斷地吸引人口及產業的進入，造成小都市相對劣勢。面對此一現象，政府應如何擬訂一適宜之都市發展政策，實有深入研究之必要。因此，本研究欲探討都市規模與都市生產力二者間之關係，希望從都市生產力之觀點，能提供合適的都市發展政策，以供政府參考之用。 　　針對上述，本研究擬利用Alonso及Schaefer都市模型來探討影響台灣地區都市規模變化之因素，進一步利用Translog生產函數求其規模報酬，藉以判斷都市生產力之高低。最後，則利用Translog生產函數與資本、勞動二條報酬分額方程式透過ISUR來探討不同都市規模要素報酬之問題。 　　經本研究實證結果得到以下幾個結論： 　　一、外部因素如：周圍地區對都市中心產品之需求、生產更高階產品之規模效果是影響都市規模、生產力變化之重要因素。 　　二、從連續型生產函數可得台灣地區都市規模與都市生產力二者間有正向關係的存在。 　　三、因限資料問題，無法進一步分析台灣地區生產要素所得分配問題。 　　四、針對不同規模報酬狀態的都市，有以下幾點都市發展建議: 　　　1.面對處於遞減規模報酬狀態的都市，應透過階層的提高，吸引各種經濟活動的進入，以提昇其生產力。 　　　2.面對處於固定規模報酬狀態的都市，應減少政府干預政策。 　　　3.面對處於遞增規模報酬狀態的都市，應避免外部不經濟的產生。 / The advantages of large urban areas, such as high degree agglomeration economy、high production, attract the entrance of population and industries. Thar will result in the disadvantage of the small city. How does the goverment play? The purpose of this study is to discuss the relationship between urban size and urban area production. Further more, I would like to give some suggestions about urban development policy. 　　In this study, I uses Alonso's and Mills's urban model to discuss what kind of factor will effect urban size and production. Next, I judge urban production through the returns of scale of different urban hierarchy, and use the translog production and the share functions of capital and labour to discuss the income distribution offactors. 　　According to the result of this study, we have several solutions: 　　1.External factor will effect the change of urban size and urban production. 　　2.According to the continuous hierarchy, there is a positive relationship between urban size and urban production. 　　3.Because of data, we can't discuss the income distribution of factor in Taiwan. 　　4.Regarding urban development policies: 　　　(1) when urban area exhibits DRS, it is necessary to move up the hierarchy for continuing urban growth. 　　　(2) when urban area exhibits CRS, goverment should not interrept urban development. 　　　(3) when urban area exhibits IRS, goverment should not generate external diseconomy.

本研究以探討高雄都會區產業型態與其空間分佈為目的，首先界定研究地理範圍與產業範圍，且對產業型態與空間分佈的分析方法及其相關研究作一番介紹，並以此對高雄都會區的產業型態、產業結構及空間分佈情形作一通盤性探討。 　　本研究內容係以區位商數分析高雄都會區基礎性產業；以CES生產函數探討高雄都會區產業型態；以產業百分比、地方化係數、產業雜異化指標來探討高雄都會區產業結構及其空間分佈；以因子分析法來探討高雄都會區產業活動影響因素；最後以群落分析來劃分高雄都會區產業整體活動、工業活動、服務業活動等中心階層。 　　本研究結果為高雄都會區基礎性產業計有木竹製品業等廿七類，而該等產業經濟型態主要為地方化經濟型態；高雄都會區產業結構以工業為主，但服務業活動已佔了相當大比重；高雄都會區產業活動主要集中於高雄市區，高雄都會區產業發展極不均衡；高雄都會區整體產業活動中心階層以楠梓區、三民區、前鎮區為主要活動中心，高雄都會區工業活動主要分佈在高雄市外緣區域如楠梓區、前鎮區、小港區，而服務業活動主要分佈於高雄市中心區域如三民區、新興區、前金區等。

創新是廠商生存於快速全球化及競爭激烈的環境中的關鍵。而廠商創新績效的影響因素可以分為外部因素與內部因素進行探討。就外部因素而言，本研究同時從經濟地理學門的區域聚集效果與社會學門的研發網絡關係探討外部環境對廠商創新績效的影響。並探討地區產業聚集現象是否會增加區域內廠商形成研發網絡的可能性。而除了廠商外部環境會影響廠商創新績效外，管理學門提出廠商的內部吸收能力也同樣重要。吸收能力定義為廠商對外部知識的認識、吸收和應用的能力。吸收能力除了會對創新績效產生直接影響之外，也會對由網絡中所獲得的外部知識的認知、吸收和利用產生調節效果。 本研究以台灣的ICT產業為研究對象，而空間單位劃分則依據工業區分布情形與天然及人為界線分佈，將台灣劃分為39個空間分析單元，以供實證分析所需。研究結果發現，廠商所處地區之聚集效果確實會對廠商研發網絡的形成產生影響，進而影響廠商所能吸收的外部知識流的多寡，最終造成不同區域的廠商創新績效的不同。廠商的研發網絡會隨群聚內的社會經濟狀況、產業組成和多樣性等不同而有所不同。此外，本研究同時從廠商外部環境的聚集效果、研發網絡關係以及廠商內部的吸收能力探討對廠商創新績效的影響，以期更全面地了解創新績效的影響因素。本研究的實證結果證實了聚集效果、研發網絡與廠商內部吸收能力確實對廠商創新績效產生影響，而內部吸收能力確實會對經由研發網絡所獲取的外部知識和創新績效產生調節效果。 / Innovation is the key of the firm to survive in a rapidly globalizing and competitive environment. The factors affecting firms’ innovation performance can be divided into external and internal factor. For the external factors, this studies use the view of regional agglomeration effects and R&D networks to study the impact of external environment on innovation performance. In the same time, we also discuss whether the regional agglomeration effects affect the firms’ R&D networks. Aside from the external environment, the internal absorption capacity is also important for innovation performance. Absorption capacity is defined as the capacity of firm to recognize, absorb and apply external knowledge. Absorption capacity has not only direct impact on innovation, but also adjusted effects between the knowledge acquired from R&D networks and innovation performance. The object of this study is the ICT industry in Taiwan, and Taiwan was divided into 39 spatial units for empirical analysis. The empirical results indicate that the regional agglomeration effects of firms indeed influence the firms’ R&D network ,and then affect the amount of the external knowledge that the firm can absorb, ultimately result in different innovation performance. Firms’ R&D networks will vary depends on the cluster’s socio-economic conditions, industry composition and diversity. Besides, this study also discusses the impact factor of firms’ innovation performance from the external agglomeration effects, R&D networks, and internal absorption capacity to have a more comprehensive understanding of the relationship between those factors and the innovation performance. The empirical results indicate that agglomeration effects, R&D networks, absorption capacity do affect the firms’ innovation performance, and the internal absorption capacity do have adjusted effects between the knowledge acquired from R&D networks and innovation performance.

Hamilton (1989)發展出馬可夫轉換模型，假設模型母體參數會隨某一無法觀察得到的狀態變數變動而改變，並用馬可夫鏈的機制來掌控狀態間切換，可適當掌握金融與經濟變數所面臨的結構改變，因此是一個十分重要的財務模型。Schwert (1989)觀察股價波動狀況，發現經濟衰退期的股價波動比經濟擴張期大，因此認為Hamilton (1989)所提出的馬可夫轉換模型亦可應用於股票市場。然而，發現當市場上有重大訊息來臨時，大部分標的資產報酬率會產生跳躍現象，因此汪昱頡 (2008)提出跳躍風險下馬可夫轉換模型，以改善馬可夫模型所無法反映之股價不正常跳躍現象。在探討股價指數報酬率之敘述統計量與動態圖後，本文認為跳躍幅度也會受狀態影響，因此進一步拓展周家伃 (2010)跳躍獨立風險下狀態轉換模型，期望對股市報酬率動態過程提供更佳的分析。實證部分使用1999到2010年的國際股價指數之S&P500、道瓊工業指數與日經225三檔作為研究資料，來說明股價指數具有狀態轉換及跳躍的現象，並利用EM(Expectation Maximization)演算法來估計模型的參數，以SEM(Supplemented Expectation Maximization )演算法估計參數的標準差，且使用概似比(Likelihood ratio)檢定結果顯示跳躍相關風險下狀態轉換模型比跳躍獨立風險下狀態轉換模型更適合描述股價指數報酬率。最後，驗證跳躍相關風險下狀態轉換模型能捕捉其報酬率不對稱、高狹峰與波動聚集之特性。 / Hamilton (1989) proposed Markov switching models to suppose the model parameters change with unobserved state variables which control the switch between states by Markov chain. It can be appropriate to grasp the financial and economic variables which facing structural changes, so it’s a very important financial model. Schwert (1989) observed stock prices, and discovered that the volatilities of recession are higher than the volatilities of expansion. Hence, Schwert (1989) suggested to apply the Markov switching models to stock market. However, most of underlying asset return have jump phenomenon when abnormal events occur to financial market. Wong (2008) proposed Markov switching models with jump risks to improve Markov switching models which can not capture the jump risk of asset price. According to stock index return’s descriptive statistics and dynamic graph, we argue that states will impact jump sizes. In this paper, we extend the regime-switching model with independent jump risks (Chou, 2010) to provide better analysis for the dynamic of return. This paper use stock indices of the study period from 1999 to 2010 to estimate the parameters of the model and variance of parameter estimators by Expectation-Maximization (EM) algorithm and SEM(Supplemented Expectation Maximization ) , respectively. And use the likelihood ratio statistics to test which model is appropriate.Finally, the empirical results show that regime-switching model with jump sizes dependency risk can capture leptokurtic feature of the asset return distribution and volatility clustering phenomenon.

本論文研究目的在於瞭解台灣IC產業聚集以及透過聚集構成的技術合作、專利引用網絡對於創新能力的影響，並且試圖回答以下的研究問題：台灣IC產業的地區空間分佈呈現什麼樣的型態?是否呈現空間的聚集性?台灣IC產業的技術合作網絡呈現何種區域化特性?台灣IC產業的上、中、下游，技術合作與競爭網絡的模式有何差異?台灣IC產業的聚集特性、技術合作與技術競爭網絡的性質，對創新的影響為何? 透過分析IC上市公司於2001~2009年的組織特性、技術合作契約與專利引用資料，本論文得到以下主要研究結論：(一) 台灣的IC業除了高比例聚集在新竹科學園區之外，在技術合作、專利授權等正式契約合作關係中也會傾向與台灣北部、美國矽谷與東北的聚集對象合作。(二) 台灣IC產業在技術合作與專利引用方面均具備高度網絡聯結的性質，且明顯有中游IC製造廠商帶動上游IC設計商與下游IC封測商發展的特性。(三) 比起產業聚集，技術網絡更能解釋影響IC廠商創新能力的因素，與較多不同地區的對象合作、掌握關鍵專利的廠商，創新能力的投入(研發經費)、產出(核准專利)與強度(技術優勢)越強。

本研究利用GARCH動態過程的優點捕捉匯率報酬率之異質變異與波動度叢聚性質，並以GARCH動態過程為基礎，考慮跳躍風險服從Lévy過程，再利用特徵函數與快速傅立葉轉換方法推導出GARCH-Lévy動態過程下的歐式匯率選擇權解析解。以日圓兌換美元(JPY/USD)之歐式匯率選擇權為實證資料，比較基準GARCH選擇權評價模型與GARCH-Lévy選擇權評價模型對市場真實價格的配適效果與預測能力。實證結果顯示，考慮跳躍風險為無限活躍之Lévy過程，即GARCH-VG與GARCH-NIG匯率選擇權評價模型，不論是樣本內的評價誤差或是在樣本外的避險誤差皆勝於考慮跳躍風險為有限活躍Lévy過程的GARCH-MJ匯率選擇權評價模型。整體而言，本研究發現進行匯率選擇權之評價時，GARCH-NIG匯率選擇權評價模型有較小的樣本內及樣本外評價誤差。 / In this thesis, we make use of GARCH dynamic to capture volatility clustering and heteroskedasticity in exchange rate. We consider a jump risk which follows Lévy process based on GARCH model. Furthermore, we use characteristic function and fast fourier transform to derive the currency option pricing formula under GARCH-Lévy process. We collect the JPY/USD exchange rate data for our empirical analysis and then compare the goodness of fit and prediction performance between GARCH benchmark and GARCH-Lévy currency option pricing model. The empirical results show that either in-sample pricing error or out-of-sample hedging performance, the infinite-activity Lévy process, GARCH-VG and GARCH-NIG option pricing model is better than finite-activity Lévy process, GARCH-MJ option pricing model. Overall, we find using GARCH-NIG currency option pricing model can achieve the lower in-sample and out-of sample pricing error.

本研究從Leech的「禮貌原則」和Grice的「合作原則」切入討論台灣著名的反迷（anti-fan）虛擬聚集處（virtual togetherness）「吐槽版」中的異議表達。研究者初步觀察吐槽版發現，該版呈現高度言論趨同的現象、鮮見異議表達。過往反迷相關文獻指出，相對於外在強勢的主流演藝圈文化，反迷意識到自己的小眾性、弱勢性，因而可能發展出成員間緊密的小團體情誼、友善互助的人際關係。研究者欲探究，在吐槽版高度趨同的發言氛圍下，版友該如何表達異議？什麼樣的異議表達方式符合吐槽版的禮貌標準？此外，吐槽版發言高度趨同、鮮見異議、鮮見網路戰火（flaming），確為出自於版友間同仇敵愾的團結情誼、友善的人際關係嗎？ 研究發現，吐槽版版友間人際關係淡漠，完全推翻研究者對於吐槽版反迷的想像。吐槽版版友雖偏好採用溫和、委婉的方式表其異見，但若時機適當，仍會出現相對尖銳的異議；且不乏針對第一輪發言者立論的「手法」表達異議，帶有「找碴」的意味。此外，版友在表達異議時，不時出現「非護航」自清，版友不自覺會將「表達異議」等同於「護航」。「禁止護航」這版規才是約束吐槽版版友異議表達的最主要力量，而非版內的人際關係。「禁止護航」保障了版友完整情緒發洩的權利，版友在此可單方面地表達對於演藝對象的厭惡之情而不會受到他人反駁。吐槽版版友無意與他人進行「雙向溝通」，完整的、單方面的「情緒宣洩」才是他們對於吐槽版的期待。版友無意集結版眾力量共同對抗演藝圈，他們對抗的其實是「理性溝通」的概念。研究者發現，在網路社群、虛擬聚集中，人們並非只需要理性溝通，「情緒發洩」也是人們重視的發言需求。

藉由建築物之改變，可以觀察都市發展轉變之過程。在一個都市經過完全開發之後，都市內部就會開始出現建築物重開發。林享博（1993）與俞國華（2010）的研究指出，建築物之重開發並非是因為建築物損壞至無法使用，而另有原因存在。本研究藉由過去之相關文獻觀察各都市建築物重開發之情形，歸納出建築物重開發可能影響因素。 本研究選取臺北市為研究範圍，使用臺北市建築管理處核發之建築物使用執照及拆除執照計算建築物之重開發程度，以里為最小空間單元，運用空間分析方法觀察臺北市建築物重開發之空間分佈模式，並運用迴歸分析方法觀察各種因素對於臺北市建築物重開發之影響。經實證研究發現，臺北市之建築重開發會受到地區之戶口數變動率、所得水準、平均屋齡、捷運站、政府主導土地開發等因素影響；戶口數變動率與政府主導之土地開發分別代表了市場力量以及政府力量，則為影響建築物重開發最主要的兩個因素。曾經發生過區段徵收或市地重劃等政府主導土地開發計畫之地區，將形成建築物重開發之高-高空間聚集，而捷運規劃這類重大交通建設則會加速此情形之發生。 / Urban development can be exemplified by means of replacement of old buildings by new ones. Once sites in a city are fully developed, the old buildings will need to be demolished for vacant sites to be supplied. It is not just the physical obsolescence that leads to teardowns of buildings, a number of other factors are also at play. In this study, we select the occupancy permits and demolition permits of buildings issued by Taipei city government to calculate net supply of floor spaces. This net supply serves as a proxy variable of building replacement in regression models. We also employ spatial analysis to measure local spatial clustering of building activities. Our empirical results show that building replacement is affected by changes in households, income level, building ages, access to metro stations and government-led land development projects. Amongst them, changes in households and government-led land development projects are two primary contributing factors. Building activities tend to cluster in areas where government-led land development projects are located, and public transport (metro lines) intensifies this tendency.

本研究以Lévy過程為模型基礎，考慮Merton Jump及跳躍強度服從Hawkes Process的Merton Jump兩種跳躍風險，利用Particle Filter方法及EM演算法估計出模型參數並計算出對數概似值、AIC及BIC。以S&P500指數為實證資料，比較隨機波動度模型、Variance Gamma模型及兩種不同跳躍風險對市場真實價格的配適效果。實證結果顯示，隨機波動度模型其配適效果勝於Variance Gamma模型，且加入跳躍風險後可使模型配適效果提升，尤其在模型中加入跳躍強度服從Hawkes Process的Merton Jump，其配適效果更勝於Merton Jump。整體而言，本研究發現，以S&P500指數為實證資料時，SVHJ模型有較好的配適效果。 / This paper, based on the Lévy process, considers two kinds of jump risk, Merton Jump and the Merton Jump whose jump intensity follows Hawkes Process. We use Particle Filter method and EM Algorithm to estimate the model parameters and calculate the log-likelihood value, AIC and BIC. We collect the S&P500 index for our empirical analysis and then compare the goodness of fit between the stochastic volatility model, the Variance Gamma model and two different jump risks. The empirical results show that the stochastic volatility model is better than the Variance Gamma model, and it is better to consider the jump risk in the model, especially the Merton Jump whose jump intensity follows Hawkes Process. The goodness of fit is better than Merton Jump. Overall, we find SVHJ model has better goodness of fit when S&P500 index was used as the empirical data.

Black and Scholes (1973)對於報酬率提出以B-S模型配適，但B-S模型無法有效解釋報酬率不對稱高狹峰、波動度微笑、波動度叢聚、長記憶性的性質。Merton (1976)認為不尋常的訊息來臨會影響股價不連續跳躍，因此發展B-S模型加入不連續跳躍風險項的跳躍擴散模型，該模型可同時描述報酬率不對稱高狹峰和波動度微笑兩性質。Charles, Fuh and Lin (2011)加以考慮市場狀態提出狀態轉換跳躍模型，除了保留跳躍擴散模型可描述報酬率不對稱高狹峰和波動度微笑，更可以敘述報酬率的波動度叢聚和長記憶性。本文進一步拓展狀態轉換跳躍模型，考慮不連續跳躍風險項的帄均數與市場狀態相關，提出狀態轉換跳躍相關模型。並以道瓊工業指數與S&P 500指數1999年至2010年股價指數資料，採用EM和SEM分別估計參數與估計參數共變異數矩陣。使用概似比檢定結果顯示狀態轉換跳躍相關模型比狀態轉換跳躍獨立模型更適合描述股價指數報酬率。並驗證狀態轉換跳躍相關模型也可同時描述報酬率不對稱高狹峰、波動度微笑、波動度叢聚、長記憶性。最後利用Esscher轉換法計算股價指數選擇權定價公式，以敏感度分析模型參數對於定價結果的影響，並且市場驗證顯示狀態轉換跳躍相關模型會有最小的定價誤差。 / Black and Scholes (1973) proposed B-S model to fit asset return, but B-S model can’t effectively explain some asset return properties, such as leptokurtic, volatility smile, volatility clustering and long memory. Merton (1976) develop jump diffusion model (JDM) that consider abnormal information of market will affect the stock price, and this model can explain leptokurtic and volatility smile of asset return at the same time. Charles, Fuh and Lin (2011) extended the JDM and proposed regime-switching jump independent model (RSJIM) that consider jump rate is related to market states. RSJIM not only retains JDM properties but describes volatility clustering and long memory. In this paper, we extend RSJIM to regime-switching jump dependent model (RSJDM) which consider jump size and jump rate are both related to market states. We use EM and SEM algorithm to estimate parameters and covariance matrix, and use LR test to compare RSJIM and RSJDM. By using 1999 to 2010 Dow-Jones industrial average index and S&P 500 index as empirical evidence, RSJDM can explain index return properties said before. Finally, we calculate index option price formulation by Esscher transformation and do sensitivity analysis and market validation which give the smallest error of option prices by RSJDM.