本論文利用Teräsvirta (1994)提出的平滑移轉自我回歸模型 (Smootth Transition Autoregressive, STAR)調查期貨幣政策對美國不動產投資信託 (Real Estate Investment Trust, REITs)收益之影響。由於REIT具有固定收益證券與股票特性的商品，對市場利率具有高度的利率敏感性，尤其近年來各國政府採行寬鬆貨幣政策，故投資人面對貨幣政策對於REIT報酬所帶來的影響，實有必要瞭解。本論文的研究目的為:1.再次檢驗REIT報酬的利率敏感性；2.採用LSTAR或ESTAR來捕捉隨著市場利率變化下，報酬對利率的調整速度是否呈現不對稱;3.採用移動平均(rolling window) 捕捉利率的調整速度不對稱，其門檻值所呈現的數值變化、特性以及調整速度。4. 最後觀察門檻值數值變化的差異性。樣本期間由2007年1月至2020年2月，資料來自Bloomberg與Capital IQ。實證結果顯示用10年期美國國債作為轉換參數，存在不對稱性，同時具調整速度，但1年期美國國債作為轉換參數時則無，即REIT報酬對10年期利率具敏感性，對1年期則無，這是由於降息導致失去利率敏感性。證明具有時間變異特性，因此可解釋過去文獻對於是否存在利率敏感性的不一致結果。此外，用滾動迴圈估計觀察γ的特性後，實證結果顯示在升息前，γ就開始顯著，顯示投資人在升息前的預測心理。

The thesis employs Smooth Transition Autoregressive by Teräsvirta (1994), to investigate the impact of monetary policy on the returns of Real Estate Investment Trusts (REITs) in the U.S.. REITs have characteristics of fixed-income securities and stocks, they have a high interest rate sensitivity to market interest rates, especially governments have adopted loose monetary policies. The objectives of the thesis are: 1. to re-examine the interest rate sensitivity of REIT returns; 2. to capture the asymmetric adjustment speed of returns to interest rates with changes in market interest rates by using LSTAR or ESTAR; 3. using a rolling window estimation captures the changes in the threshold value, characteristics.4. to explain how investors respond to these market volatilities The sample period is divided into five subperiods to observe the variability of the threshold values. The sample period is from January 2007 to February 2020, and the data is obtained from Bloomberg and Capital IQ. The empirical results show REIT returns are sensitive to the 10-years interest rate, but not to the 1-year interest rate, which is because of the loss of interest rate sensitivity due to interest rate cuts. It suggests its time-varying characteristics, thus explaining the inconsistent results in the previous literature. The rolling window estimation to observe the characteristics of γ, the empirical results show that γ starts to be significant before the interest rate is raised. It Shows investors' prediction before the interest rate rise.

Chapter 1 Introduction	1
  1.1 The development and environment of REITs	1
  1.2 Motivation	4
  1.3 Objectives	6
  1.4 Structure	8
  1.5 Illustration of the structure	10
Chapter 2 Literature Rview	11
  2.1 The interest rate sensitivity on the REIT returns	11
  2.2 The impact of monetary policy on REIT returns	14
  2.3 The role of REITs during crisis	18
  2.4 The application of Smooth Transition Autoregressive model	20
Chapter 3 Methodology	23
  3.1 Variables definitions	23
  3.2 Verification of unit-root 	25
  3.3 Methodology 	28
    1 Smooth Transition Autoregressive (STAR) model	28
    2 Exponential function	29
    3 Logistic function	29
  3.4 Model testing	31
    1 Setting of linear model and selection of optimal lag lengths...	31
    2 Determination of optimal lag lengths and the non-linear test	31
    3 Determining the LSTAR or ESTAR models	32
    4 The dynamic variation of threshold value and transition variable	32
Chapter 4 Data Source and Processing 	34
  4.1 Data source	34
  4.2 Basic statistics analysis	36
Chapter 5 Empirical Results and Analysis	42	
  5.1 The unit-root test	42
  5.2 Optimal lengths of AR(p)	43
  5.3 Estimation of ESTAR model	44
Chapter 6 Conclusion	50
References	52

List of Tables
  Table 1 The periods of contractionary and expansionary monetary	36
  Table 2 The descriptive statistics of the variables of whole period and interest related subperiod	39
  Table 3 The descriptive statistics of the variables of events	41
  Table 4 ADF and PP unit-root test	42
  Table 5 The results of the STAR model F test and model determination	43
  Table 6 The results from ESTAR (10-years interest rate)	45
  Table 7 The results from ESTAR (1-year interest rate)	46

List of Figures
  Figure 1 FTSE Nareit Real Estate Index historical number of REITs	2
  Figure 2 FTSE Nareit Real Estate Index historical market capitalization	2
  Figure 3 The illustration of the structure	10
  Figure 4 The rolling window estimation	33
  Figure 5 The value of transition function over sample period	47
  Figure 6 The variation of transition coefficient over 2015/1/2 to 2015/12/31	48
  Figure 7 The variation of transition coefficient over 2016/1/2 to 2018/12/31	49

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