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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.

This study includes three issues about the dynamic of 30-days Taiwan Commercial Paper rate (CP2).The first issue focuses on the estimation of continuous-time short-term interest rate models. We discretize the continuous-time models by using two different approaches, and then use weekly and monthly data to estimate the parameters. The models are evaluated by data fit. We find that the estimated parameters are similar for different discretization approaches and would be more stable and efficient under quasi-maximum likelihood (QML) with weekly data. There exists mean reversion for Taiwan CP rate and the relationship between the volatility and the level of interest rates are less than 1 and smaller than that of American T-Bill rates reported by CKLS (1992) and Nowman (1997). We also find that CIR-SR model performs best for Taiwan CP rate. The second issue compares the continuous-time short-term interest rate models empirically both by predictive accuracy test and encompassing test. Having the estimated parameters of the models by discretization of Nowman(1997) and QML, we produce the forecasts on conditional mean and volatility for the interest rate over multiple-step-ahead horizons. The results indicate that the sophisticated models outperform the simpler models in the in-sample data fit, but have a distinct performance in the out-of-sample forecasting. The models equipped with mean reversion can produce better forecasts on conditional means during some period, and the heteroskedasticity variance model with outperform counterparts in volatility forecasting in some periods. The third issue concerns the persistent and massive volatility of short-term interest rates. This part inquires how the realizations on Taiwan short-term interest rates can be best described empirically. Various popular volatility specifications are estimated and tested. The empirical findings reveal that the mean reversion is an important characteristic for the Taiwan interest rates, and the level effect exists. Overall, the GARCH-L model fits well to Taiwan interest rates.

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.

Hidden Markov models have become a popular tool for modeling long-term investment guarantees. Many different variations of hidden Markov models have been proposed over the past decades for modeling indexes such as the S&P 500, and they capture the tail risk inherent in the market to varying degrees. However, goodness-of-fit testing, such as residual-based testing, for hidden Markov models is a relatively undeveloped area of research. This work focuses on hidden Markov model assessment, and develops a stochastic approach to deriving a residual set that is ideal for standard residual tests. This result allows hidden-state models to be tested for goodness-of-fit with the well developed testing strategies for single-state models. This work also focuses on parameter uncertainty for the popular long-term equity hidden Markov models. There is a special focus on underlying states that represent lower returns and higher volatility in the market, as these states can have the largest impact on investment guarantee valuation. A Bayesian approach for the hidden Markov models is applied to address the issue of parameter uncertainty and the impact it can have on investment guarantee models. Also in this thesis, the areas of portfolio optimization and portfolio replication under a hidden Markov model setting are further developed. Different strategies for optimization and portfolio hedging under hidden Markov models are presented and compared using real world data. The impact of parameter uncertainty, particularly with model parameters that are connected with higher market volatility, is once again a focus, and the effects of not taking parameter uncertainty into account when optimizing or hedging in a hidden Markov are demonstrated.

過去文獻在探究股市報酬率波動行為時，多採用GARCH/ARCH等傳統時間序列模型，但這些模型不能解決波動度的高持續性(persistence)。本文以Gray(1996)提出的一般化狀態轉換模型(GRS-GARCH)為基礎並加入Dueker(1997)所提出的Dispersion設定，建立GRS-GARCH-K以及GRS-GRACH-DF模型來預測股市報酬率波動行為。GRS-GARCH-K模型設定最大的優點是加入Student’s t分配之自由度可隨狀態轉換，使峰態亦可隨狀態轉換，另外GRS-GRACH-DF模型除了擁有GRS-GARCH-K的特性外，還擁有均數復歸的特色。本文以單一狀態下的GARCH-N、GARCH-t模型，以及雙狀態下的GRS-GARCH、GRS-GARCH-K以及GRS-GARCH-DF模型做研究，並以台灣股價加權股價指數為研究樣本，探討並預測股價日報酬率的波動度，最後將波動度代入Black-Scholes選擇權訂價模型，探討模型之其評價效果。 研究顯示，在樣本內以AIC和SBC檢定法則下，GRS-GARCH-DF有最好的配適能力，樣本外的預測能力在MAE、MASE、MAPE三種誤差比較法下，GRS-GARCH-DF相較於GARCH-N、GARCH-t、GRS-GARCH和GRS-GARCH-K四種模型，在訂價方面與市場價格誤差最小，並以DM檢定法證實其統計上的顯著性。因此擁有均數復歸特色的GRS-GARCH-DF在波動度的估計上相較於其他模型來的優異。

@Frankfurt (Main), University, Diss., 2007.