製藥業和其他消費性用品產業有許多不同，像是需要大量地投資於新產品研發與創新。許多藥品和其他消費品相比，有較長的產品生命週期，而且藥品購買者通常並非是藥品使用者。儘管藥品有較長的生命週期，最終仍會面對專利到期的一天。對於製藥業而言，近年來的市場准入(market access)變得更具挑戰性，臨床試驗的障礙、法規核可，和健保給付核可價格也變得比以前困難。藥品需要較長時間以準備進入市場，因此在專利到期前的市場銷售期也縮短許多。如何在有限的市場銷售期極大化銷售業績和利潤對於藥品行銷者而言，也日益重要。即使是在專利過期後，如何管理以及延展藥品生命週期對於製藥業也是一門學問。 為了縮短藥品上市前的準備期，製藥公司需要了解管制法規的申請策略，和選擇最適宜者。下一步是思考健保價格策略。了解中央健康保險局的機制，可以有效減少反覆送件的過程。 度過藥品上市前的準備期後，下一個任務是如何延展產品生命週期。常見策略有1)增加原藥品的新適應症；2)推出機轉類似，但稍微改良過的新產品，以替換即將專利保護過期的藥品；3)將即將專利保護過期的舊產品在劑型上導入新科技，而此新劑型具有專利保護;4)導入固定劑量複方藥品;5)投資學名藥。 產品生命週期的管理對於製藥業非常重要，公司應該強化縮短產品上市前準備期間的能力，在市場銷售期極大化銷售業績與利潤，並在專利到期後延展產品生命週期。 對健康保險環境的研究，以及醫生行為受保險體制的改變，與長時間對人們健康的影響仍待未來進行進一步地探討。 / Pharmaceutical industry is quite different from other consumer products industry. It needs heavy investment on product development and innovation. Most of drugs have longer lifecycle than consumer products, and usually the drug buyer is not drug user. Though drug has long lifecycle, eventually it will face patent-off. Recently market access becomes more and more challenging for pharmaceutical industry. The barriers of clinical trials, regulatory approval, and reimbursement price approval are more difficult than before. Drugs need longer time to go-to-market, and the period of commercialization before patent-off is shorter. How to maximize sales and profits within limited commercialization time becomes more critical for marketers. And how to well manage drug lifecycle and extend lifecycle even after patent-off are the other crucial lessons for industry. In order to shorten “go-to-market” period, drug company needs to understand the regulatory submission strategies, and choose the most appropriate one for submission. Next step is considering reimbursement price strategies. Understanding Bureau of National Health Insurance’s (BNHI) mechanism can minimize back-and-forth process. After “go-to-market”, the next task is how to extend product lifecycle. The most common methods are 1) launch new indication, 2) launch new/ improved generation to replace old drug, 3) launch new dosage/ presentation form, which have patent to extend compound patent, 4) introduce fix-dosed combination, 5) invest in generics. Lifecycle management is critical for pharmaceutical industry. Company should strengthen the competencies to shorten product “go-to-market” period, maximize the sales and profits during commercialization, and extend lifecycle after patent-off. The research of health insurance environment, the changes of physicians’ behaviors and impacts on people health need further studies.

長期性資料(longitudinal data)的最主要特徵是為對多個被觀測個體在不同的時間點上重複測量一個或多個反應變數。而在分析長期性資料的方法中，Laird & Ware(1982)建議以線性混合效果模型(linear mixed effects model，LME)來進行估計分析，此模型方法中，資料可以允許遺失值，並可將受測個體間與個體內的變異分開說明。 另在配適最小平方法(OLS)的迴歸模型中，係數估計經常會受到異常值的影響，而Rousseeuw & Leroy(1987)提出最小消去平方法(least trimmed squares，LTS)的穩健迴歸模型，即是解決最小平方法中對於異常值敏感的問題。 本研究主要針對台灣股票預期報酬之三種模型：資本資產定價模型、特徵模型、因子模型分別以OLS、LTS、LME三種估計方法做配適，並比較配適模型之適當與否，樣本資料為民國七十年七月至九十年六月共252個月516家上市公司股票報酬。實證結果顯示，不論是採用OLS、LTS、LME的估計方法，股票報酬解釋變數：系統風險、公司規模、帳面權益對市值比、SMB、HML皆為股票報酬的顯著解釋因子；而在模型比較方面，不論是配適資本資產定價模型、特徵模型或因子模型，LME都較OLS為較適當配適模型。這顯示了在分析長期性資料時，LME的確是一個較佳的統計分析模型。

資本資產定價模型 (CAPM) 由Sharp (1964)、Lintner (1965)及Black (1972)發展出後，近年來已被廣泛的應用於衡量證券之預期報酬率與風險間之關係。一般而言，衡量結果之估計有兩個階段，首先由時間序列分析估計出貝它(beta)係數，然後再檢定廠商或投資組合之平均報酬率與貝它係數之關係。 Fama與MacBeth (1973)利用最小平方法估計貝它係數，再將由橫斷面迴歸方法所得出之斜率係數加以平均後，以統計t-test檢定之。然而以最小平方法估計係數，其估計值很容易受離群值之影響，因此本研究考慮以穩健估計 (robust estimator)來避免此一問題。另外，本研究亦將長期資料分析 (longitudinal data analysis) 引入CAPM裡，期望能檢定貝它係數是否能確實有效地衡量出系統性風險。 論文中以台灣股票市場電子業之實證分析來比較上述不同方法對CAPM的結果，資料蒐集期間為1998年9月至2001年12月之月資料。研究結果顯示出，穩健估計相對於最小平方法就CAPM有較佳的解釋力。而長期資料分析模型更用來衡量債券之超額報酬部分，是否會依上、中、下游或公司之不同而不同。 / The Capital Asset Pricing Model (CAPM) of Sharp (1964), Lintner (1965) and Black (1972) has been widely used in measuring the relationship between the expected return on a security and its risk in the recent years. It consists of two stages to estimate the relationship between risk and expected return. The first one is that betas are estimated from time series regressions, and the second is that the relationship between mean returns and betas is tested across firms or portfolios. Fama and MacBeth (1973) first used ordinary least squares (OLS) to estimate beta and took time series averages of the slope coefficients from monthly cross-sectional regressions in such studies. However it is well known that OLS is sensitive to outliers. Therefore, robust estimators are employed to avoid the problems. Furthermore, the longitudinal data analysis is applied to examine whether betas over time and securities are the valid measure of risk in the CAPM. An empirical study is carried out to present the different approaches. We use the data about the Information and Electronic industry in Taiwan stock market during the period from September 1998 to December 2001. For the time series regression analysis, the robust methods lead to more explanatory power than the OLS results. The linear mixed-effect model is used to examine the effects of different streams and companies for the security excess returns in these data.

過去許多企業，將企業原本擁有的智慧財產（例如：專利、商標、營業祕密…）與企業的其他資產、負債，分離出來，成立智慧財產控股公司，並透過智慧財產供股公司的經營管理，獲取大量的租稅利益。此一租稅規劃工具雖然已引起稽徵機關的注意，但運用得當，仍可為企業創造可觀的利潤。 智慧財產控股公司的設立架構，母公司通常會在低稅率的國家或州，設立一完全控股的子公司，由智慧財產控股公司自行創設、或自母公司繼受智慧財產。智慧財產控股公司授權的對象，可能是母公司、亦可能為不相干的第三人。 智慧財產控股公司的租稅效益，來自智慧財產控股公司通常選在低公司稅率（甚至零稅率）的地區設立，對於權利金收入予以免稅的地區。母公司付給子公司的權利金費用，母公司可作為費用扣除，藉以降低母公司的所得稅。智慧財產控股公司可透過發放股利，或對母公司融資等方式，解決母公司的資金需求。 透過智慧財產控股公司進行租稅規劃，最重要面臨『移轉定價』與『避免濫用租稅協定』，因此智慧財產控股公司進行的關係人交易，不能是純為獲取租稅利益的假交易，必須有商業實質。 / Over the last decade or so, many businesses generating significant revenue from intellectual property such as patents, copyrights, trade names and marks, software and know-how (the IP Assets) have organized intellectual property holding companies (IPHCs) to reduce federal and state taxes while separating valuable IP Assets from other corporate liabilities. Recently, states have started to aggressively challenge this tactic. However, substantial state and federal tax savings can still be realized if IPHCs are organized and operated correctly. The structure of an IPHC is fairly simple. The parent corporation typically creates a corporate subsidiary in a state or in a foreign country where little or no taxes are imposed . IP Assets are created by or transferred to the subsidiary. The subsidiary enters into license agreements under which the parent corporation and non-related corporations agree to pay the IPHC royalties in exchange for an exclusive or non-exclusive right to use the IP Assets. Since most IPHCs are organized in jurisdictions with no income tax, the royalties received by the IPHC are generally tax-free. In addition, the parent corporation that paid the royalty typically can deduct the payment as a deductible expense, thereby reducing the parent's income or franchise tax liability. In some circumstances, IPHCs can make tax-free dividend distributions or loans to the parent corporation. The key issue IPHC should consider is “Transfer Price Issue” and “Anti Treaty Shopping Issue”. Transactions between related parties can’t be shame transaction, business substance is required.

本研究目的欲探討實施費率自由化第一及第二階段後之情形，即在2006年第二階段實施後，台灣產物保險公司及各險種個別之資金成本，以檢視兩階段自由化實施後是否顯著影響國內產險業。而資金成本為公司每段期間內應支付資金提供者之期望報酬，故以此可做為日後公司經營之參考指標。研究期間為2002年至2008年，分別由一因子模型及多因子模型解釋台灣產物保險業之資金成本，及系統風險(β)的變化是否會影響其資金成本之變動。利用資本資產定價模型(Capital Asset Pricing Model, CAPM)及Fama-French三因子模型(Fama-French Three-Factor Model, FF3F)求得公司資金成本，再透過完備資訊方法(The Full-information Industry Beta Method, FIB)了解不同險種間之系統風險及資金成本。實證結果顯示： 1. 無論在整體產險公司或是不同險種間，由FF3F模型所估計之資金成本均高於由CAPM模型所估計之資金成本。說明CAPM模型無法反映公司規模及財務危機因子(淨值市價比因子)之溢酬，而造成資金成本之低估。 2. 經CAPM模型及FF3F模型之估計，顯示台灣產險業之資金成本均低於國外產險業之資金成本，如美國。說明台灣產險業於資本市場之融資成本較低，造成其資本效率偏低，投資人變相縱容產險公司從事高風險性資產之投資。 本研究由台灣實證資料，顯示現行產險業資金取得成本低，導致其資本效率偏低，且投資人無法由市場資訊檢視其保險本業是否根據成本之考量來定價，故主管機關應於費用完全自由化後，加強產險業經營之監理，導正產險市場經營模式，避免因核保循環(underwriting cycle)而影響公司財務穩健。 關鍵詞：費率自由化、資金成本、資本資產定價模型、Fama-French三因子模型、完備資訊方法。

本篇論文旨在分析神經網路學習績效，並提出一套學習演算法，結合倒傳遞網路(BP)與理解神經網路(RN)，命名為RNBP，這套學習演算法將與傳統的BP做比較，以兩個不同的財務金融領域的應用，一個是選擇權上Black-Scholes訂價模式的模擬，一個是金融交換上利率的預測。主要績效的評估準則是以學習的效率與模擬、預測的準確度為依據。 此外，本論文的另一個重點是提出一套對於神經網路系統進一步分析的方法與工具，敏感度分析(Sensitivity Analysis)與滯留區(Dead Region)分析，藉以瞭解神經網路系統是否具有效地良好學習或被一般化的能力，從神經網路的角度來說，這也是BP與RNBP的另一個績效比較標準。本研究的結果顯示RNBP在預測準確度上較BP為優良，但是在學習效率與預測能力的穩定性上並沒有呈現一致性的結論；此外，敏感度分析與滯留區分析的結果也幫助神經網路在應用領域上有更深入的瞭解。 在過去，神經網路的應用者往往忽略了進一步瞭解神經網路的重要性與可行性，本論文的貢獻在於藉由分析神經網路所學習的知識，幫助應用者進一步瞭解神經網路表達的訊息在應用領域上所隱含的實質意義。 / The study attempts to analyze the learning performance of neural networks in applications, and propose a new learning procedure for the layered feedforward neural network systems, named KNBP, which binds RN and BP learning algorithms. Two artificial neural networks, BP and KNBP, here are both applied to two financial fields, the simulation of Black-Scholes pricing model for the call options and the midrates forecasting in financial swaps. The explicit performance comparison between the two artificial neural network systems is mainly based on two criteria, which are learning efficiency and forecasting effectiveness. Then we propound a mathematical methodology of sensitivity analysis and the dead regions to deeply explore inside the network structures to see whether the models of ANNS are actually well trained or valid, and thus setup an alternative comparable criterion. The results from this study show that RNBP performs better than BP in forecasting effectiveness, but RNBP obtains neither a consistent learning efficiency in cases nor a stable forecasting ability. Furthermore, the sensitivity analysis and the dead region analysis provide a deeper view of the ANNs in the applied fields. In the past, most studies applying neural networks ignored the importance that it is feasible and advantageous to obtain more useful information via analyzing neural networks. The purpose of the research is to help further understanding to the information discovery resulted from neural networks in practical applications.

第一筆金融交換出現以來，短短的十一、二年 場成長迅速，成為不可或 缺的財務工具。有鑑鷟艦瘣城竣@簡要的介紹，並建立金融交換之定 珓洶妨堨腄A主要承襲S. Sundaresan 對金融交bS. Sundaresan 的研究中 ，只針對一般的金融交A未考慮特殊型態的金融交換。所以本文的目的在B 下利率限制的金融交融之定價模型，主要定價的般的、capped、floored 、及 collared金融交換。漱隤k上，採用與其他研究不同的Feynman-Kac So- 融交換定價模型之前，必須先建立一般的金融交C再利用利率caps 和floors之特性，加入一般金融A以推導出有上下利率限制的金融交換定 價模型。F導出金融交換之定價模型外，並對所建立的模型k，計算出金融 交換和collar的價值，同時分析@般金融交換與collar金融交換的價值。 提供銀j眾，在進行金融交換時作為參考。

Black-Scholes選擇權定價模型是各種選擇定價的開山始祖，無論在理論或實務上均獲致許多的便利及好評，美中不足的是，這種既定模型下結構化參數的估計問題，在真實體系的結構訊息未知或是不明朗時，或是模式錯誤，亦或政治結構或金融環境不知時，該模型在實證資料的評價上會面臨價格偏誤的窘境。是故，許多的數值演算法（numerical algorithms）便因應而生，這些方法一則源於對此基本模型的修正，一則是屬於逼近的數值解。 評價選擇權的方法雖不一而足，然所有的這些理論或模型可分為二大類即模型驅動的理論（model-drive approach）及資料驅動的理論（data-driven approach）。前者是建構在許多重要的假設，當這些假設成立時，則選擇權的價格可用如Black-Scholes偏微分方程來表示，而後再用數值解法求算出，許多的數值方法即屬於此類的範疇；而資料驅動的理論（data-driven approach），其理論的特色是它的有效性（validity）不像前者是依其假設，職是之故，他在處理現實世界的財務資料時更顯見其具有極大的彈性。這些以計算智慧（computation intelligence）為主的財務計量方法，如人工神經網路（ANNs），遺傳演算法（GAs），遺傳規劃（GP）已在財務工程（financial engineering）領域上萌芽，並有日趨蓬勃的態勢，而將機器學習技術（machine learning techniques）應用在衍生性商品的定價，應是目前財務應用上最複雜及困難，亦是最富挑戰性的問題。 本文除了對現有文獻的整理評析外，在人工神經網路方面，除用於S&P 500的實證外，並用於台灣剛推行不久的認購構證評價之實證研究；而遺傳規劃在計算智慧發展的領域中，算是較年輕的一員，但發展卻相當的快速，雖目前在經濟及財務上已有一些文獻，但就目前所知的二篇文獻選擇權定價理論的文獻中，仍是試圖學習Black-Scholes選擇權定價模型，而本文則提出修正模型，使之成為完全以資料驅動的模型，應用於S&P 500實證，亦證實可行。最後，本文結合計算智慧中的遺傳演算法（ genetic algorithms）及數學上的加權殘差法（weight-residual method）來建構一條除二項式定價模型，人工神經網路定價模型，遺傳規劃定價模型等資料驅動模型之外的另一種具適應性學習能力的選擇權定價模式。 / The option pricing development rapid in recent years. However, the recent rapid development of theory and the application can be traced to the pathbreaking paper by Fischer Black and Myron Scholes(1973). In that pioneer paper, they provided the first explicit general equilibrium solution to the option pricing problem for simple calls and puts and formed a basis for the contingent claim asset pricing and many subsequent academic studies. Although the Black-Scholes option pricing model has enjoyed tremendous success both in practice and research, Nevertheless, it produce biased price estimates. So, many numerical algorithms have advanced to modify the basic model. I classified these traditional numerical algorithms and computational intelligence methods into two categories. Namely, the model-driven approach and the data-driven approach. The model-driven approach is built on several major assumptions. When these assumption hold, the option price usually can be described as a partial differential equation such as the Black-Scholes formula and can be solved numerically. Several numerical methods can be regarded as a member of this category. There are the Galerkin method, finite-difference method, Monte-Carlo method, etc. Another is the data-driven approach. The validity of this approach does not rests on the assumptions usually made for the model-driven one, and hence has a great flexibility in handling real world financial data. Artificial neural networks, genetic algorithms and genetic programming are a member of this approach. In my dissertation, I take a literature review about option pricing. I use artificial neural networks in S & P 500 index option and Taiwan stock call warrant pricing empirical study. On the other hand, genetic programming development rapid in recent three years, I modified the past model and contruct a data-driven genetic programming model. andThen, I usd it to S & P 500 index option empirical study. In the last, I combined genetic algorithms and weight-residual method to develop a option pricing model.

資本資產定價模型已被廣泛使用於分析股票風險與要求報酬率之間的關係。然而，個別股票風險Beta是否足以解釋其報酬，也受到愈來愈多的質疑。Fama和French在1993年提出額外兩個因子來解釋股票報酬。我們將應用資本資產定價模型和三因子模型來分析1963年7月至2002年12月之美國的三大股票交易所上市公司。藉由一次改變分析過程中的一部分，以觀察參數估計值是否穩定。結果發現Beta_HML總是顯著且最為穩定，而Beta_SMB並不顯著。Beta經常顯著，但變動情況較大。另外，我們將考慮個別股票本身的變異，亦即將隨機效果納入考量。 / The Capital Asset Pricing Model (CAPM) has been widely used to analyze the relationship between risk and required rate of return on a stock, while it is doubted that individual stock's risk Beta has enough explanatory power for it's returns. Fama and French (1993) proposed two more factors to help explaining stock returns. We use the CAPM and the three-factor model to analyze listed companys in American stock exchanges, during the period from July 1963 to December 2002. We change part of the analyzing process a time to see if the estimates of the parameters are stable. The risk-premium Beta_HML is always significant and it performs most stable, while another risk-premium Beta_SMB is never significant. Beta is usually significant but it varies. Furthermore, we take within-stock variation into account, so random effects are considered.

本文使用Esscher轉換法推導狀態轉換模型、跳躍獨立風險下狀狀態轉換模型及跳躍相關風險下狀態轉換模型的選擇權定價公式。藉由1999年至2011年道瓊工業指數真實市場資料使用EM演算法估計模型參數並使用概似比檢定得到跳躍相關風險下狀態轉換模型最適合描述報酬率資料。接著進行敏感度分析得知，高波動狀態的機率、報酬率的整體波動度及跳躍頻率三者與買權呈現正相關。最後由市場驗證可知，跳躍相關風險下狀態轉換模型在價平及價外的定價誤差皆是最小，在價平的定價誤差則略高於跳躍獨立風險下狀態轉換模型。 / In this paper, we derive regime switching model, regime switching model with independent jump and regime switching model with dependent jump by Esscher transformation. We use the data from 1999 to 2011 Dow-Jones industrial average index market price to estimate the parameter by EM algorithm. Then we use likelihood ratio test to obtain that regime switching model with dependent jump is the best model to depict return data. Moreover, we do sensitivity analysis and find the result that the probability of the higher volatility state , the overall volatility of rate of return , and the jump frequency are positively correlated with call option value. Finally, we enhance the empirical value of regime switching model with dependent jump by means of calculating the price error.