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系統識別號 U0002-0206200921285400
中文論文名稱 應用不同之計量方法研究REITs之報酬與波動性
英文論文名稱 The Research of REITs Return and Volatility via Alternative Econometric Approaches
校院名稱 淡江大學
系所名稱(中) 財務金融學系博士班
系所名稱(英) Department of Banking and Finance
學年度 97
學期 2
出版年 98
研究生中文姓名 白東岳
研究生英文姓名 Tung-Yueh Pai
學號 894490142
學位類別 博士
語文別 英文
第二語文別 中文
口試日期 2009-05-09
論文頁數 94頁
口試委員 指導教授-邱建良
共同指導教授-李命志
委員-林蒼祥
委員-蕭峰雄
委員-梁發進
委員-俞海琴
委員-黃博怡
委員-謝文良
委員-李命志
中文關鍵字 不動產投資信託  狀態轉換  不對稱均衡  波動性預測 
英文關鍵字 REITs  Regime-change  Asymmetric Equilibrium  Volatility forecasting 
學科別分類 學科別社會科學商學
中文摘要 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。
英文摘要 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.
論文目次 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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PART II
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PART III
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