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.

Neste trabalho analisaremos a utilização dos modelos de mudança de regime markoviano para a variância condicional. Estes modelos podem estimar de maneira fácil e inteligente a variância condicional não observada em função da variância anterior e do regime. Isso porque, é razoável ter coeficientes variando no tempo dependendo do regime correspondentes à persistência da variância (variância anterior) e às inovações. A noção de que uma série econômica possa ter alguma variação na sua estrutura é antiga para os economistas. Marcucci (2005) comparou diferentes modelos com e sem mudança de regime em termos de sua capacidade para descrever e predizer a volatilidade do mercado de valores dos EUA. O trabalho de Hamilton (1989) foi uns dos mais importantes para o desenvolvimento de modelos com mudança de regime. Inicialmente mostrou que a série do PIB dos EUA pode ser modelada como um processo que tem duas formas diferentes, uma na qual a economia encontra-se em crescimento e a outra durante a recessão. O câmbio de uma fase para outra da economia pode seguir uma cadeia de Markov de primeira ordem. Utilizamos as séries de índice Bovespa e S&P500 entre janeiro de 2003 e abril de 2012 e ajustamos o modelo GARCH(1,1) com mudança de regime seguindo uma cadeia de Markov de primeira ordem, considerando dois regimes. Foram consideradas as distribuições gaussiana, t de Student e generalizada do erro (GED) para modelar as inovações. A distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos se mostrou superior à distribuição normal para caracterizar a distribuição dos retornos em relação ao modelo GARCH com mudança de regime. Além disso, verificou-se um ganho no percentual de cobertura dos intervalos de confiança para a distribuição normal, bem como para a distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos, em relação ao modelo GARCH com mudança de regime quando comparado ao modelo GARCH usual. / In this work we analyze heterocedastic financial data using Markov regime switching models for conditional variance. These models can estimate easily the unobserved conditional variance as function of the previous variance and the regime. It is reasonable to have time-varying coefficients corresponding to the persistence of variance (previous variance) and innovations. The economic series notion may have some variation in their structure is usual for economists. Marcucci (2005) compared different models with and without regime switching in terms of their ability to describe and predict the volatility of the U.S. market. The Hamiltons (1989) work was the most important one in the regime switching models development. Initially showed that the series of U.S. GDP can be modeled as a process that has two different forms one in which the economy is growing and the other during the recession. The change from one phase to another economy can follow a Markov first order chain. We use the Bovespa series index and S&P500 between January 2003 and April 2012 and fitted the GARCH (1,1) models with regime switching following a Markov first order chain, considering two regimes. We considered Gaussian distribution, Student-t and generalized error (GED) to model innovations. The t-Student distribution with the same freedom degree for both regimes and distinct degrees showed higher than normal distribution for characterizing the distribution of returns relative to the GARCH model with regime switching. In addition, there was a gain in the percentage of coverage of the confidence intervals for the normal distribution, as well as the t-Student distribution with the same freedom degree for both regimes and distinct degrees related to GARCH model with regime switching when compared to the usual GARCH model.

In order to catch up the international trend, ¡§Expensing employee bonus¡¨ has been implemented in Taiwan since year 2008. Hence, all the cost concerning employees¡¦ bonuses have been recorded as expense in the income statement and recognized by fair market value. Since the company decides total amount of employees¡¦ bonuses after authorized by the board and annual general meeting, it can distribute the proportion of cash and stock bonuses. As the result of calculating the stock bonus by stock¡¦s fair value, employees gain much less stocks than before, which lessen the encouragement effect. Therefore, enterprises begin to increase the standard salary of employee or proportion of cash bonus. This study collects the data from the fourth quarter of year 1989 to the third quarter of 2009, and chooses the Taiwan Weighted Stock Index and the stock prices of listed electronic firms in Taiwan. Using the Markov Regime Switching Model as the research method, and add the macroeconomic and financial variables to separate the stock price into two regimes- recession and expansion regime. This research is in the employee¡¦s shoes, and to study what stock incentives strategies the company should adopt under the required expensing of employee stock bonus. The empirical results are summarized as follows: 1.Under the expansion regime, if the company¡¦s stock price was affected by both macroeconomic and financial variables, it will more likely rise further, which leads to the large gap between two regimes. For example: Cyberlink, Acer and Mediatec, which stock price gaps are over ten dollars. 2.According to the two arguments of this study: the company with long duration of expansion regime and is influenced by macroeconomic and financial variables should adopt the strategies based on stock bonus. Therefore, according to the empirical results, the study suggests that Acer is the suitable company to do the strategies.

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In this paper, we use a Fama-French model and Markov regime-switching model to capture time series behavior of many financial variable. Alternatively, classification by cluster analysis help to learn the different characteristics of the sample between stock returns and risk factors. This empirical result shows that the excess return in the low volatility state tends to be greater than that in the high volatility state. The stock returns in each regime have a higher probability of remaining in their original state, especilly in low volatility state. This article also found the influence of risk factors affecting the stock returns is not symmetrical. In the state of low volatility, market factors and momentum effect have a significant influence in stock returns, and in the high volatility state, except the size effect, market and behavior factors have a significant influence in stock returns. Markov-switching models have proved to be useful for modeling a range of economic time series in the stock market. The regime-switching model has a superior performance in capturing the risk sensitivities of the stock return beyond the findings based on the Fama-French models. At last, we find the cluster analysis is feasible for the multi-factor model. The returns of mature companies have a primarily impact of market risk premium, while the major factor affecting returns with characteristics of growth companies is a investor sentiment. In addition, it is found that small companies¡¦ returns are vulnerable to investors sentiment. In this case, investors will invest based on stock's past performance, so the momentum effect significantly affect the stock returns.

In order to get maximum profit, investors start to high attention on risk management after financial crisis in 2008. Therefore, risk management and predict become more and more complex. This paper mainly focuses on two risks, including non-systematic risk and credit risk. After financial crisis, countries pay more attention on credit risk, and now because of Europe debt crisis, investors and governments are also concerned with the messages about credit rating which are published by Credit Rating Agency. Besides credit risk, the firm¡¦s specific risk (i.e. non-systematic risk) is also more important than before. Recent empirical studies find that the stock is not on affected by systematic risk, but also affected by non-systematic risk. According to Kuo and Lu (2005), this thesis uses two models: Moody¡¦s KMV credit model and Markov regime switching model to estimate credit risk and non-systematic risk. The period is from January 2002 to November 2010. Testing samples are data from constituent stocks of the Taiwan 50. The purpose of this paper is researching the relationship between credit risk and non-systematic risk. The empirical results show that there is the positive relationship between non-systematic risk and credit risk. And among different industries, non-systematic risk or credit risk also shows the significant differences. For plastic industry and communications network industry, there is lower credit risk. However, for electronics industry and financial industry, there is higher credit risk. The study also found that even in the same industry, each company will face different risk level.

Since reformed of banking structure in China, banks have been gradually developed their operation system. Moreover, the restructure in commercial bank after joined WTO had established China¡¦s banks performance and international reputation. Since 2007, many large commercial banks have strength its risk management based on the commitments made by China Banking Regulatory Commission (CBRC) to follow the New Basel Capital Accord. When the global banking industry is devastated by global financial crisis (GFC) during 2008, China¡¦s banks are less affected by GFC. In addition, the capital scale and revenues performance were thrived during GFC. Therefore, it shows that banks in China had improved the resilience ability during financial crisis. However, being originated in China¡¦s loose monetary policy and economic stimulus package after GFC, investors worried that domestic banks might bear high risks. Notably, the risk is specific risk from each bank instead of system risk. This study employs Markov regime-switching model to examine 14 China banks¡¦ stock prices. The empirical evidence supports our hypothesis that behavior of China banks¡¦ stock prices has confronted structural change after GFC. Furthermore, this research presents that unsystematic risks from each bank were significantly decreased after GFC. It indicates that investors are too pessimistic on the banks in China might suffer high risk after government interventions.

This study tires to find out how a firm¡¦s advertising and customer satisfaction influence firms¡¦ abnormal return and we uses the abnormal return (i.e. Jensne¡¦s £\) as the proxy of firm¡¦s shareholder value. We expect firms¡¦ advertising and customer satisfaction will have a positive impact on abnormal return while having a negative impact on firms¡¦ risk. In addition, we also consider under different market state whether advertising and customer satisfaction have an asymmetric effect. Compare with Carhart (1997) four factor model, this paper also takes the factor of VIX into account, and we use Markov regime switching model to recognize bull market and bear market because it can help us get a more accurate estimation. We choose the Generalized method of moments (GMM) to estimate the impact of advertising and customer satisfaction on shareholder value and discuss that whether advertising and customer satisfaction are able to lift up shareholder value or not. The outcome shows that advertising doesn¡¦t have significantly positive impact on firms¡¦ abnormal return under bull market and bear market. However, customer satisfaction has a significantly positive relationship with firms¡¦ abnormal return under bull market and bear market. And we find that if firms maintain the level of customer satisfaction under bear market, it will be more efficiently to lift up firms¡¦ abnormal return rather than spending more money on advertising.

Neste trabalho analisaremos a utilização dos modelos de mudança de regime markoviano para a variância condicional. Estes modelos podem estimar de maneira fácil e inteligente a variância condicional não observada em função da variância anterior e do regime. Isso porque, é razoável ter coeficientes variando no tempo dependendo do regime correspondentes à persistência da variância (variância anterior) e às inovações. A noção de que uma série econômica possa ter alguma variação na sua estrutura é antiga para os economistas. Marcucci (2005) comparou diferentes modelos com e sem mudança de regime em termos de sua capacidade para descrever e predizer a volatilidade do mercado de valores dos EUA. O trabalho de Hamilton (1989) foi uns dos mais importantes para o desenvolvimento de modelos com mudança de regime. Inicialmente mostrou que a série do PIB dos EUA pode ser modelada como um processo que tem duas formas diferentes, uma na qual a economia encontra-se em crescimento e a outra durante a recessão. O câmbio de uma fase para outra da economia pode seguir uma cadeia de Markov de primeira ordem. Utilizamos as séries de índice Bovespa e S&P500 entre janeiro de 2003 e abril de 2012 e ajustamos o modelo GARCH(1,1) com mudança de regime seguindo uma cadeia de Markov de primeira ordem, considerando dois regimes. Foram consideradas as distribuições gaussiana, t de Student e generalizada do erro (GED) para modelar as inovações. A distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos se mostrou superior à distribuição normal para caracterizar a distribuição dos retornos em relação ao modelo GARCH com mudança de regime. Além disso, verificou-se um ganho no percentual de cobertura dos intervalos de confiança para a distribuição normal, bem como para a distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos, em relação ao modelo GARCH com mudança de regime quando comparado ao modelo GARCH usual. / In this work we analyze heterocedastic financial data using Markov regime switching models for conditional variance. These models can estimate easily the unobserved conditional variance as function of the previous variance and the regime. It is reasonable to have time-varying coefficients corresponding to the persistence of variance (previous variance) and innovations. The economic series notion may have some variation in their structure is usual for economists. Marcucci (2005) compared different models with and without regime switching in terms of their ability to describe and predict the volatility of the U.S. market. The Hamiltons (1989) work was the most important one in the regime switching models development. Initially showed that the series of U.S. GDP can be modeled as a process that has two different forms one in which the economy is growing and the other during the recession. The change from one phase to another economy can follow a Markov first order chain. We use the Bovespa series index and S&P500 between January 2003 and April 2012 and fitted the GARCH (1,1) models with regime switching following a Markov first order chain, considering two regimes. We considered Gaussian distribution, Student-t and generalized error (GED) to model innovations. The t-Student distribution with the same freedom degree for both regimes and distinct degrees showed higher than normal distribution for characterizing the distribution of returns relative to the GARCH model with regime switching. In addition, there was a gain in the percentage of coverage of the confidence intervals for the normal distribution, as well as the t-Student distribution with the same freedom degree for both regimes and distinct degrees related to GARCH model with regime switching when compared to the usual GARCH model.

The dissertation proposes three approaches for financial risk management. In the first topic, we investigate the stock return and risk of financial holding companies via Markov regime-switching model. The model reduces the disadvantage of traditional linear model, which disregard information of another regime if there exist structural change during the estimation periods. The empirical result shows that all financial holding companies have different stock risk between state 0 and state 1. Moreover, stock risks of all financial holding companies are significant lower after listing. That is, financial holding companies have diversification benefits after listing. In the second topic, we gauge the credit risk of guarantee issue in a bills finance company in Taiwan by a market-based model. Since bills finance companies engage in short-term loans, we renew the contract that can extend short-term loans to mid-and long-term loans. We find that the recovery rate, different industries and business cycle have significant impact on the credit risk of the bills finance company. In the third topic, we relax the assumption of Jarrow, Lando and Turnbull (1997), and propose an elaborate model to gauge the credit risk of Taiwanese bank loans. The empirical result indicates that the credit risk is heavily reliant on the recovery rate. Therefore, collateral value check procedure is very important, which has been found in previous topic. On the other hand, we find that the credit risk management is indifferent between banks participated in financial holding companies and others. That is, banks do not have better credit risk management if take part in financial holding companies. In conclusion, we expect approaches of the dissertation will be helpful for Taiwan¡¦s financial institutions to rise to the challenge of financial risk in the future.