None

ABSTRACT With the development of high-tech industry, the power quality has become a critical issue for the industrial customers in science park. The voltage sag and power system stability problems due to fault contingency in Taipower network has caused serious production loss. The manufacturing process platforms, which are driven by power electronics equipments may shutdown when the voltage dip exceeds 30% and it will take long time for the restoration of production. To enhance the service reliability and power quality, the new cogeneration system in Hsin Chu Science Park has been selected for case study to solve the problems of short circuit capacity and voltage sag. The short circuit analysis by both ANSI and IEC is performed to find the magnitudes of fault currents. The transient stability analysis is executed to identify the critical clearing time to support the design of protective relays for tie line tripping. The static var compensator (SVC) is also considered in the simulation to investigate the mitigation of system voltage drop due to fault contingency. It is found that the implementation of cogenerators and SVC can improve the electricity service quality for high-tech customers with proper design of industrial power systems.

This investigates the voltage flicker problem of a large steel plant and presents the mitigation strategy by applying the static var compensator (SVC) and cogenerator. The fluctuation of real power and reactive power consumption by an arc furnace has been measured and recorded during the steel production process. The dynamic load model of the A/C arc furnace is derived based on the actual field data and has been included in the computer simulation by the CYME software package for load flow analysis. The block diagrams of SVC controller and the excitation system of cogenerators are considered to solve the response of reactive power compensation according to the voltage fluctuation of the control bus. To maintain the electric service reliability of arc furnace when an external utility fault occurs, the tie line tripping and load shedding is implemented to prevent the tripping of cogenerator after system disturbance. It is found that the dynamic load behavior of arc furnace in the isolated industrial power system can be well compensated by the cogenerator with adaptive control of exciter and governor to generate proper reactive power and real power according to the fluctuation of bus voltage and system frequency respectively.

None

none

In the literature, it is a common belief that the U.S. stock market is the single most influential market in the world. The U.S. stock market is a global factor, affecting both developed and emerging markets. This dissertation empirically investigates the interactions between equity markets of the Asian four little dragons (Hong Kong, Korea, Singapore, and Taiwan) and the U.S. equity market. In order to assess correctly the effect of the U.S. stock return rates on emerging equity markets, we incorporate the assumption that returns on the U.S. stock market affect the stock returns on emerging markets but not vice versa. In other words, it is assumed that the U.S. stock exchange performance is not affected by one of the four Asian equity market; however, the latter is affected by both its own dynamics and the U.S. stock exchange. This dissertation consists of three essays. In order to estimate the dynamic impulse responses of the emerging markets¡¦ return rates to random shocks in the U.S. return rates, the first essay uses block exogenous VAR models which suggested in the papers of Zha (1996), Cushman and Zha (1997), and Zha (1999), and it finds that return rates on the U.S. positively affect stock return rates of the four Asian markets. By using the method of Rapach and Wohar (2005a, 2006a), and the second essay also finds that return rates on the U.S. have in-sample and out-of-sample predictive ability for return rates of the respective emerging market. The last essay follows the econometric methodology of Bai and Perron (1998, 2003a, 2003b, and 2004) and it points out that there exists at least one structural change in the predictive regression model of the respective empirical equity market. The results suggest that an emerging equity market¡¦s sensitivity to shocks from the U.S. return rates is related to its degree of openness.

In retrospect, telecommunication technology and services have seen incessant renovation and development. The wave of liberalization is also the inexorable trend in the global telecommunications industry, the telecommunications industry in Taiwan can not be excluded itself from the trend. The telecommunications industry in Taiwan has been opened by degrees and sought to establish a fair competitive environment. In the meantime, there are several important changes no matter in facets of regulatory regimes, industrial structure, technology, or market demand, etc. The environment of telecommunications industry became more volatile than the monopoly one's. We extend the opinion of Noam (2006) who observed the long-term upturn and downturn in the American telecommunications industry and concluded that that volatility and cyclicality will be an inherent part of the telecommunication sector in the future. First, in our thesis we explore the cyclical behavior of Taiwan telecommunications industry. As the turning point of the telecommunications industry may be obscure, we adopt a Markov Regime-Switching model with two regimes representing contraction and expansion. This nonlinear, two states, regime-switching model shows that Taiwan telecommunications industry has suffered from the cyclic fluctuation since the liberalization had been followed out. We focus on the mobile phone industry thereafter in this study. Since three telecommunication-related laws passed in 1996, the mobile phone industry is the first industry implemented the liberalization policy. In the process of the mobile phone industry's evolution, the carriers in this industry all experience the rapid growth in the mobile phone penetration rate and the fierce competition. Hence, to identify the main explanatory factors of the mobile phone industry fluctuation and cycles we introduce an 11-variable vector autoregressive (VAR) model. The empirical results confirm that the mobile phone industry' output can be influenced by five factors mainly including the macroeconomic status, demand, network effect, relative equipment import price, and output price, and furthermore, the impetus of the liberalization policy and the progress of the technology also play an important role beyond the five main factors in terms of the separate carriers' analysis.

風險值(Value at Risk, VaR)為衡量金融風險最重要之工具，而由於許多文獻皆實證指出金融資產報酬率為厚尾分配，導致傳統上假設報酬率為常態分配將會低估金融資產所面對之下方風險，因此須運用極值理論結合風險值估計來捕捉厚尾，提升風險值估計之準確性。 本研究使用簡單加權移動平均法下之Normal VaR模型與VaR-x模型，及在指數加權移動平均法下之EWMA VaR-x模型來估計股票、外匯及投資組合之風險值，並進行回顧測試及失敗率檢定以評估模型準確性，實證結果指出以VaR-x表現最佳，其模型失敗率皆無顯著異於理論失敗率。然而結果亦指出EWMA VaR-x之模型失敗率過低，可能存在高估風險值的問題，但若投資標的為較厚尾之金融資產時，其失敗率卻相當接近於理論失敗率。

Texte remanié de: Th. doct.--Sci. polit.--Nice, 2000. Titre de soutenance : Le Front national à Toulon, vécus et schèmes de représentation : analyse d'entretiens non-directifs. / En appendice, choix de documents. Bibliogr. p. 385-397.

Pastaruoju metu, investavimui tampant vis populiaresniu, atsiranda poreikis skaičiuoti portfelių rizikuojamąją vertę (angl. Value at Risk, toliau tekste VaR). Pastaroji gali būti skaičiuojama portfeliams sudarytiems iš skirtingų finansinių instrumentų. Tačiau iškyla problemų, kai finansiniai instrumentai yra tarpusavyje susiję (priklausomi). Šiai situacijai išspręsti naudojame VaR, kuris skaičiuojamas jungčių (angl. Copula) pagalba. Darbo tikslas – nagrinėjamiems portfeliams parinkti jungtis, kurios geriausiai atspindėtų bendrą duomenų pasiskirstymą. Tada, turint jungtis, apskaičiuoti VaR. Gavome, kad vertinant 1 portfelį ateinančiu laiko momentu mūsų didžiausias tikėtinas nuostolis yra intervale tarp 4.34 ir 4.70 litų. 2 portfelio nuostolis yra intervale (2.88, 3.42), 3 portfelio – (3.29, 5.28 ). / Recently, investments acquire vogue and it’s necessary to compute the Value at Risk of portfolio. VaR can be computed for portfolio which is made from different finance instruments. But the problem arises when these instruments are interdependent. In order to solve this problem, we compute VaR using copulas. The aim of this work is to pick copulas for real data which is the best for the distribution of the data. At that point compute VaR using selected copulas. The results are: in future time the biggest loss for first portfolio is in the interval 4.43 ant 4.7 Litas, for second portfolio the biggest loss – (2.88, 3.42) ant for third portfolio – (3.29, 5.28).