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.

Asset allocation and portfolio decisions are at the heart of money management and draw great attention from both academics and practitioners. In addition, the segmentation of fund investors (i.e., the clientele effect) in the money management industry is well known but poorly understood. The objective of this dissertation is to study the implications of regime switching behaviors in asset returns on asset allocation and to analyze the clientele effect as well as the impact of portfolio management contracts on fund investment. Chapter 2 presents an innovative regime switching multi-factor model accounting for the different regime switching behaviors in the systematic and idiosyncratic components of asset returns. A Gibbs sampling approach for estimation is proposed to deal with the computational challenges that arise from a large number of assets and multiple Markov chains. In the empirical analysis, the model is applied to study sector exchange-traded funds (ETFs). The idiosyncratic volatilities of different sector ETFs exhibit a strong degree of covariation and state-dependent patterns, which are different from the dynamics of their systematic component. In a dynamic asset allocation problem, the certainty equivalent return is computed and compared across various models for an investor with constant relative risk aversion. The out-of-sample asset allocation experiments show that the new regime switching model statistically significantly outperformed the linear multi-factor model and conventional regime switching models driven by a common Markov chain. The results suggest that it is not only important to account for regimes in portfolio decisions, but correct specification about the structure and number of regimes is of equal importance. Chapter 3 proposes a rational explanation for the existence of clientele effects under commonly used portfolio management contracts. It shows that although a fund manager always benefits from his market timing skill, which comes from his private information about future market returns, the value of the manager's private information to an investor can be negative when the investor is sufficiently more risk-averse than the manager. This suggests different clienteles for skilled and unskilled funds. Investors in skilled funds are uniformly more risk-tolerant than investors in unskilled funds. Moreover, a comparative statics analysis is conducted to investigate the effects of the manager's skill level, contract parameters, and market conditions on an investor's fund choice. The results suggest that the investors who are sufficiently more risk-averse than the manager should include fulcrum fees in the contract to benefit from the skilled manager's information advantage.

This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.

過去文獻在探究利率波動行為時多採用現貨市場利率做為研究對象，思及期貨市場交易成本較低且流動性也較高使其對新資訊的反應更為迅速下，本文改以短期利率期貨，三個月期歐洲美元定存利率期貨、三個月歐元存款利率期貨以及三十天期商業本票利率期貨的隱含利率作為樣本資料，進而探討美國、歐洲及台灣的利率波動行為。研究方法以Gray(1996)提出的一般化狀態轉換模型為基礎並加入可以反應不對稱性的Dispersion設定，此設定有二個優點，其一為當面臨極大衝擊時，可減少衝擊所造成的變異數持續性而產生波動度均數復歸的現象，此設計乃考量到樣本期間一半時期均處於高峰度狀態的情形不常見，當波動度處於高峰時，預期市場波動度會反轉成近似常態水準；其二為易於Student’s t分配之狀態轉換模型下自由度的參數化設定，使峰態可隨狀態轉換。另外亦加入槓桿效果設定來反應市場上正負消息對資產報酬波動度所造成的不對稱影響。 由AIC模型配適度選擇準則下，適合描述美國、歐洲以及台灣的利率模型分別為RS-GARCH-L-DF, RS-GJR-GARCH-L-DF與RS-GJR-GARCH模型，這三個模型在DM預測力檢定下亦顯示具較佳模型預測力，本文進一步透過此些模型來探測歷年來重大經濟事件與央行利率政策對利率波動度的影響與關聯性。 研究結果顯示美國、歐洲及台灣的利率波動行為均具有顯著的高低兩波動狀態，台灣與歐洲的利率處於高低波動期間的機率較平均，但台灣處於高波動度狀態的機率遠高於歐洲，相形之下，美國普遍處於低波動度狀態；三者的利率長期皆會回歸於某一均衡水準，且顯著存在波動度叢聚的現象，其中，台灣利率的波動最為劇烈，而美國與歐洲的利率行為則具有波動度長期會回歸某一均衡水準的現象。當利率水準較高時，可清楚窺知歐洲的利率波動度也會較大，此現象亦存在於美國的高波動時期，但不適用於台灣利率動態行為上的描述。

In this thesis, the regime dependent mean and abnormal returns are studied to examine whether socially responsible mutual funds have a different performance from traditional mutual funds, since there may be different patterns in the economy. Five economic factors - stock returns, treasury yield spread, credit spread, economic confidence and building permits - are used to identify the market regimes, which are determined as bear and bull markets. The regime-dependent abnormal returns are calculated with a regime-switching Fama & French three factor asset-pricing model. The empirical results show that socially responsible mutual funds have statistically higher mean return than non-socially responsible mutual funds in both bear and bull markets. However, using the measurement of the abnormal returns, socially responsible mutual funds statistically underperform non-socially responsible mutual funds in bull market, while the performance of the two types of funds are not statistically differentiable in the bear market.

This dissertation consists of two essays: one looks at the time-varying relationship between earnings and price momentum, and the other looks at how liquidity and transparency affect the pricing differential between Chinese A-and Hong Kong H-share. The first essay presented in Chapter I investigates the time varying relationship between earnings momentum and price momentum. Using a Markov-switching framework, allowing for variation between high volatility and low volatility states, I find that price momentum is significantly more influenced by earnings momentum in the high volatility state. Further for price momentum I find that loser firms display a higher degree of differential response to earnings momentum across the low and high volatility states than winner firms. Limited financing and investor’s sensitivity to future investment opportunities might explain these two results. A further analysis indeed indicates that loser firms tend to be more financially constrained. Additionally, I investigate the relationship between investor sentiment and the two momentums and find that sentiment only has predictive power for price momentum profits in the low volatility state. Finally, the results are robust regardless of instrument variables. The second essay presented in Chapter 2 examines the impact of liquidity and transparency on the discount attached to H-shares from 2003 to 2011. The higher the relative illiquidity of an H-share, the more the H-share is discounted relative to the underlying A-share price. In addition, more actively traded A-shares and infrequently traded H-shares are associated with a higher H-share discount. Further, increases in the number of analysts following a firm, both in the A-and H- market, are accompanied by a lower H-share discount. Also, a firm with a higher percentage of A-share holdings by mutual funds is associated with a smaller H-share discount. Overall, the results provide support for the notion that liquidity and transparency affect the relative pricing of A- and H-shares.

The purpose of this thesis is to examine the nonlinear relationships between financial (and economic) variables within the field of financial econometrics. The thesis comprises two reviews of literatures, one on nonlinear time series models andthe other one on term structure of interest rates, and four empirical essays on financialapplications using nonlinear modelling techniques. The first empirical essay compares different model specifications of a Markov switching CIR model on the term structure of UK interest rates. We find the least restricted model provides the best in-sample estimation results. Although models with restrictive specifications may provide slightly better out-of-sample forecasts in directional movements of the yields, the economic gains seem to be small. In the second essay, we jointly model the nominal and real term structure of the UK interest rates using a three-factor essentially affine no-arbitrage term structure model. The model-implied expected inflation rates are then used in the subsequent analysis on its nonlinear relationship with the FTSE 100 index return premiums, utilizing a smooth transition vector autoregressive model. We find the model implied expected inflation rates remain below the actual inflation rates after the independence of the Bank of England in 1997, and the recent sharp decline of the expected inflation rates may lend support to the standing ground of the central bank for keeping interest rates low. The nonlinearity test on the relationship between the FTSE 100 index return premiums and the expected inflation rates shows that there exists a nonlinear adjustment on the impact from lagged expected inflation rates to current return premiums. The third essay provides us additional insight into the nature of the aggregate stock market volatilities and its relationship to the expected returns, in a Markov switching model framework, using centuries-long aggregate stock market data from six countries (Australia, Canada, Sweden, Switzerland, UK and US). We find that the Markov switching model assuming both regime dependent mean and volatility with a 3-regime specification is capable to captures the extreme movements of the stock market which are short-lived. The volatility feedback effect that we studied on each of these six countries shows a positive sign on anticipating a high volatility regime of the current trading month. The investigation on the coherence in regimes over time for the six countries shows different results for different pairs of countries. In the last essay, we decompose the term premium of the North American CDX investment grade index into a permanent and a stationary component using a Markov switching unobserved component model. We explain the evolution of the two components in relating them to monetary policy and stock market variables. We establish that the inversion of the CDX index term premium is induced by sudden changes in the unobserved stationary component, which represents the evolution of the fundamentals underpinning the risk neutral probability of default in the economy. We find strong evidence that the unprecedented monetary policy response from the Fed during the crisis period was effective in reducing market uncertainty and helped to steepen the term structure of the CDX index, thereby mitigating systemic risk concerns. The impact of stock market volatility on flattening the term premium was substantially more robust in the crisis period. We also show that equity returns make a significant contribution to the CDX term premium over the entire sample period.

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.

本篇論文主要在檢視Fong與See (2001) 所提出的假說，將其應用於道瓊歐洲石油天然氣指數期貨 (DJ EURO STOXX OIL & GAS Index Futures) 上，是否能得到相同的驗證。 　　在是否加入狀態轉換考量的檢定中，本文採用AIC與BIC準則為判斷的基準，而由於雙狀態下BIC準則易有樣本參數過大的懲罰特性，因此其中又以AIC為較佳判斷的準則。研究結果顯示，有考量狀態轉換的Regime Switching GARCH模型配適度會較無考量狀態轉換的GARCH模型為佳。而在納入狀態轉換的考量下，在Regime Switching GARCH模型及其相關衍生模型的比較中，主要是採用RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t模型作為比較。這裡同樣以AIC與BIC準則為判斷的基準，研究結果顯示，在三模型中，是以RS-GARCH(1,1)-t模型具有最佳的配適度。 　　在預測能力的檢定中，本研究是利用MSE、MAE與R2，來判斷何者具有較佳的解釋能力，並且以DM檢定來進一步驗證。研究結果顯示，在有考量狀態轉換的Regime Switching GARCH模型與無考量狀態轉換的GARCH模型中，是以有考量狀態轉換的Regime Switching GARCH模型具有較佳的預測能力；而在RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t三種衍生模型的比較中，又以同時考量t分配以及有狀態轉換的RS-GARCH(1,1)-t模型具有較佳的預測能力。

A general methodology is described in which policyholder behaviour is decoupled from the pricing of a variable annuity based on the cost of hedging it, yielding two sequences of weakly coupled systems of partial differential equations (PDEs): the pricing and utility systems. The utility systems are used to generate policyholder withdrawal behaviour, which is in turn fed into the pricing systems as a means to determine the cost of hedging the contract. This approach allows us to incorporate the effects of utility-based pricing and factors such as taxation. As a case study, we consider the Guaranteed Lifelong Withdrawal and Death Benefits (GLWDB) contract. The pricing and utility systems for the GLWDB are derived under the assumption that the underlying asset follows a Markov regime-switching process. An implicit PDE method is used to solve both systems in tandem. We show that for a large class of utility functions, the two systems preserve homogeneity, allowing us to decrease the dimensionality of solutions. We also show that the associated control for the GLWDB is bang-bang, under which the work required to compute the optimal strategy is significantly reduced. We extend this result to provide the reader with sufficient conditions for a bang-bang control for a general variable annuity with a countable number of events (e.g. discontinuous withdrawals). Homogeneity and bang-bangness yield significant reductions in complexity and allow us to rapidly generate numerical solutions. Results are presented which demonstrate the sensitivity of the hedging expense to various parameters. The costly nature of the death benefit is documented. It is also shown that for a typical contract, the fee required to fund the cost of hedging calculated under the assumption that the policyholder withdraws at the contract rate is an appropriate approximation to the fee calculated assuming optimal consumption.