本論文著重於REITs資產的報酬與波動性之衡量與應用，共包含三個部份。第一部份為「不動產投資信託市場的狀態改變:美國市場實證」、第二部份為「EREITs和MREITs之間存在不對稱均衡關係」與第三部份為「GARCH-SGED之REITs波動性預測」。將此三部份的內容簡述如下。
    第一部分是在研究REITs狀態變化的特性和解釋總體經濟變數對REITs的影響，並探討EREITs和MREITs的風險和報酬之間的差異。本實證分析使用馬可夫模型，並進一步比較常態分配與SGED分配的差異。實證結果顯示，EREITs和MREITs都符合兩狀態轉變過程。特別的是，MREITs的不確定風險高於EREITs而且兩REITs的狀態持續有所差異。此外，EREITs和MREITs對於利率的敏感性也有所不同，EREITs只對長期利率有反應，MREITs則對長短期利率皆有反應。最後，本研究結果顯示兩REITs報酬都呈現高峰厚尾的特性。
    第二部份使用傳統共整與門檻共整模型，測試EREITs和MREITs長期之間是否存在均衡關係，本研究使用1972年1月到2008年1月的月指數資料進行實證分析，並進一步使用因果關係檢定應用於門檻誤差修正模型，去測試出是否有因果關係存在於EREITs和MREITs。實證結果顯示，EREITs和MREITs存在非對稱均衡關係而且兩REITs互為因果。
    最後一部分使用GARCH-N, GARCH-ST 和GARCH-SGED模型，探討REITs報酬率分配的設定對樣本外波動性預測績效的影響。實證資料採用REITs的日指數價格，其用意在於進行美國REITs市場之分析，提供投資者討論和比較的論點。實證結果顯示，不論是MSE或MAE作為比較準則，GARCH-SGED
模型在美國市場的波動性預測能力皆優於GARCH-N和GARCH-ST模型。同時，DM檢定統計量進一步證實GARCH-SGED模型顯著優於GARCH-N和GARCH-ST模型。此結果說明了偏態及厚尾特性的分配在波動性預測的重要性。

The purpose of this dissertation is to contribute to the literature on investigating return and volatility of REITs assets which comprises three parts. The first part of the dissertation is entitled “Regime Changes in Real Estate Investment Trusts Markets: Evidence from the United States Market”, the second part is named “Existence of an Asymmetric Equilibrium Relationship between Equity and Mortgage REITs”, and the last one is “REITs Volatility Prediction for Skew-GED Distribution of The GARCH Model”. A brief introduction of these three parts can be summarized as follows: 
The first part aims to explore the characteristics of regime-changes in REITs, to examine the influence of macroeconomic variables on REITs, and to investigate the differences in the risk and returns of equity REITs and mortgage REITs. The empirical analysis adopts a Markov regime-switching model and further compares the differences under normal and skewed generalized error distributions. Our overall findings show that the two REITs are sensibly modelled as a two-state regime-switching process. In particular, the uncertainty associated with risk mortgage REITs is higher than that for equity REITs, and the regime-persistence varies between the two. Moreover, the sources of interest rate sensitivity for equity and mortgage REITs are found to be different. Equity REITs are only sensitive to long-term interest rates, whereas mortgage REITs are sensitive to both changes in long- and short-term interest rates. Finally, this study shows that the two REITs returns each exhibit the types of height and fat-tails of the density function. Thus, we believe that our approach is methodologically solid and appropriate for providing a better understanding of the effects of regime-changes on the REITs markets.
The second part investigates whether a long-run relationship exists between the EREITs and MREITs via traditional and threshold co-integration testing using both monthly indexes running from January 1972 to January 2008. This study further uses Granger-causality tests based on the corresponding threshold error-correction model to assess whether causality exists between the EREITs and MREITs. The empirical results indicate that there is an asymmetric threshold co-integration relationship as well as a bidirectional feedback causality relationship between the EREITs and MREITs.
The last part investigates how specification of return distribution for REITs influences the performance of volatility forecasting using three GARCH models (GARCH-N, GARCH-ST and GARCH-SGED). Daily prices on the REIT provide an empirical sample for discussing and comparing relative ability to accurately out-of-sample volatility, given the growth potential of REIT markets in the United State from the perspective of global investors. Empirical results indicate that the GARCH-SGED model is superior to the GARCH-N and GARCH-ST model in forecasting REITs volatility in the United State, for all forecast horizons in which model selection is based on MSE or MAE. Meanwhile, the DM-tests further confirm that volatility forecasts using the GARCH-SGED model are more accurate than those generated using the GARCH-N and GARCH-ST model in all cases. These findings demonstrate the significant influences of both skewness and tail-thickness on the conditional distribution of returns.

TABLE OF CONTENTS
                                                              Page
ACKNOWLEDGEMENT                                          I
ABSTRACT IN CHINESE                                     II
ABSTRACT IN ENGLISH                                     IV
LIST OF TABLES                                           X
LIST OF FIGURES                                         XI

PART I	1
Regime Changes in Real Estate Investment Trusts Markets: Evidence from the United States Market
ABSTRACT	2
CHAPTER
1. Introduction	3
1.1 Motivations and Objectives	3
1.2 Flow Chart	9
2. Literature Review	10
2.1 The relationship between the REITs markets and the macroeconomic variables	10
2.2 Regime changes in financial assets	14
2.3 The characteristics of non-normal distribution in financial markets	16
3. Econometric Methodology	18
3.1 Data Description	18
3.2 Econometric Methodology	19
4. Empirical Results	25
4.1 Descriptive statistics	25
4.2 Empirical analysis	27
5. Concluding Remarks	35
BIBLIOGRAPHY	38
PART II	44
Existence of an Asymmetric Equilibrium Relationship between Equity and Mortgage REITs
ABSTRACT	45
CHAPTER
1. Introduction	46
1.1 Motivations and Objectives	46
1.2 Flow Chart	49
2. Literature Review	50
3. Data Description and Econometric Methodology	53
3.1 Data Description	53
3.2 Econometric Methodology	53
3.2.1 Traditional Co-integration Test	53
3.2.2 Threshold Co-integration Test	55
3.2.3 Granger-Causality Tests Based on the Threshold Error-Correction Model	56
4. Empirical Results	58
4.1 Descriptive statistics	58
4.2 Traditional Co-integration Test VS. Threshold Co-integration Test	59
4.3 Threshold Error-Correction Model	61
5. Conclusions Remarks	64
BIBLIOGRAPHY	66

PART III	70
REIT Volatility Prediction for Skew-GED Distribution of The GARCH Model

ABSTRACT	71
CHAPTER
1. Introduction	72
1.1 Motivations and Objectives	72
1.2 Flow Chart	74
2. Literature Review	75
3. Econometric Methodology	79
3.1 Data Description	79
3.2 Econometric Methodology	79
3.2.1 GARCH(1,1) model with normal, S-T and SGED distribution	80
3.3 Volatility Forecasts	81
4. Empirical Results	85
4.1 Data Description	85
4.2 Estimation Results	86
4.3 Volatility forecasting performance	88
5. Conclusions Remarks	90
BIBLIOGRAPHY	91






LIST OF TABLES
                                                                                              Page
PART I

Table I.1. Descriptive Statistics	26
Table I.2. The Unit Root Rest of Linear and Nonlinear	27
Table I.3. Estimated Results from Normal and SGED Distribution in Equity REITs	32
Table I.4. Estimated Results from Normal and SGED Distribution in Mortgage 
        REITs	33

PART II

Table II.1. Summary Statistics of EREITs and MREITs	58
Table II.2. Cointegration Test	60
Table II.3. TAR and MTAR Cointegration Test	61
Table II.4. Estimates of the Error-Correction Models	62

PART III

Table III.1. Descriptive Statistics of Daily Returns	85
Table III.2. Model Estimates with Alternate Distributions	87
Table III.3. Out-of-Sample MSE and MAE	89
Table III.4. DM Test	89



LIST OF FIGURES
Page
PART I

Figure I.1. The Behaviours of Equity REITs and Mortgage REITs in Sample Period	27
Figure I.2. Smooth Regime Probabilities for EREITs in State 1	34
Figure I.3. Smooth Regime Probabilities for MREITs in State 1	34

PART II

Figure II.1. Return of EREITs and MREITs	59
Figure II.2. Threshold estimation in TAR	60
Figure II.3. Threshold estimation in MTAR	60

PART III
Figure III.1. REIT index and REIT Index Daily Returns	86 
Figure III.2. Model Estimates with Alternate Distributions	87

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